Method for detecting or assessing an inflammatory state or condition in a canine subject and / or determining whether the canine subject is at risk of developing the inflammatory state or condition

Abstract

A method for detecting or assessing an inflammatory state or condition in a canine subject, or determining whether the canine subject is at risk of developing the inflammatory state or condition, is disclosed. The method comprises determining in a biological sample obtained from the canine subject quantitative values of the following biomarkers: - glycoprotein acetyls, and - albumin; and optionally a quantitative value of at least one biomarker of the following: - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject or of the canine subject having an increased risk of developing the inflammatory state or condition.

Description

[0001]METHOD FOR DETECTING OR ASSESSING AN INFLAMMATORY STATE OR CONDITION IN A CANINE SUBJECT AND / OR DETERMINING WHETHER THE CANINE SUBJECT IS AT RISK OF DEVELOPING THE INFLAMMATORY STATE OR CONDITION TECHNICAL FIELD The present disclosure relates generally to biomarkers and their use in detecting or assessing an inflammatory state or condition in a canine subject and / or determining whether the canine subject is at risk of developing the inflammatory state or condi- tion. BACKGROUND Inflammation is a common process observed in most dis- eases that aims in restoring normal homeostasis during and after insult. In addition to diseases primarily considered inflammatory, such as infectious diseases and autoimmune diseases, many acute and chronic diseases have a systemic inflammatory component. Suf- fering from chronic disease may cause systemic inflammation, but this is not the only link between low-grade systemic inflammation and disease. Low-grade systemic inflammation has been shown to be present in humans even a decade before disease onset, and this low-grade inflammation itself seems to contribute to the develop- ment of various diseases. Identification of an inflammatory state is an important part of a diagnostic workup. In addition to identifying the pres- ence in an inflammatory state, inflammatory markers are used to identify the severity of an inflammatory state, assess mortality and morbidity risk, and treatment outcome. Currently good tools, such as C-reactive protein (CRP), are available for assessing acute inflammation, but biomarkers for chronic low-grade inflammation in dogs are lacking. Identification of dogs suffering from chronic low-grade inflammation could yield in earlier diagnostics of chronic diseases, improving the well-being and even prolonging the lifespan of dogs. SUMMARY A method for detecting or assessing an inflammatory state or condition in a canine subject, and / or determining whether the canine subject is at risk of developing the inflammatory state or condition, is disclosed. The method may comprise determining in a biological sample obtained from the canine subject quantitative values of the following biomarkers: - glycoprotein acetyls, and - albumin; and optionally a quantitative value of at least one biomarker of the following: - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included to provide a further understanding of the embodiments and constitute a part of this specification, illustrate various embodiments. In the drawings: Fig. 1 shows metabolites associated with overall disease burden; Fig. 2 shows metabolite association with specific disease states; and Fig. 3 illustrates ROC curves and AUC of the metabolite- based multivariate logistic regression model by disease. DETAILED DESCRIPTION A method for detecting or assessing an inflammatory state or condition in a canine subject, and / or determining whether the canine subject is at risk of developing the inflammatory state or condition, is disclosed. The method may comprise determining in a biological sample obtained from the canine subject a quantitative value(s) of at least one of the following biomarkers: - glycoprotein acetyls, and - albumin, - pyruvate, - a ratio of phenylalanine to tyrosine, phenylalanine, and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may comprise determining in a biological sample obtained from the canine subject quantitative values of the following biomarkers: - glycoprotein acetyls, and - albumin; and optionally a quantitative value of at least one biomarker of the following: - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. With the method, it may be possible to assess whether the canine subject is suffering from an inflammatory state or condition. Thus, the method may be a method for detecting or assessing an inflammatory state in the canine subject. The method may be a method for detecting or assessing an inflammatory condition in the canine subject, for example pyometra, acute gastroenteritis, urinary tract infection, canine infectious respiratory disease, atopic dermatitis, parodontitis, osteoarthritis, and / or a neoplastic disease, such as cutaneous neoplasia or noncutaneous neoplasia. The method may allow for detecting the presence of an inflammatory state in the canine subject prior to the actual onset of an inflammatory condition. It may, in other words, be possible to detect or assess an inflammatory state in the canine subject and thereby determine whether the canine subject is at risk of developing an inflammatory condition, for example pyometra, acute gastroenteritis, urinary tract infection, canine infectious respiratory disease, atopic dermatitis, parodontitis, osteoarthritis, and / or a neoplastic disease. The method may be capable of detecting the inflammatory component of the inflammatory condition prior to the (full) onset of the inflammatory condition, and thereby may allow for treating, followup or further diagnostics of the inflammatory condition at early stages or prior to the actual inflammatory condition or onset thereof. The method may comprise determining in the biological sample quantitative values of at least two, or at least three biomarkers of the following: - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The values of the at least two, or at least three biomarkers of the above may be determined in addition to glycoprotein acetyls and albumin. The method may comprise determining in the biological sample quantitative values of glycoprotein acetyls and albumin, as well as pyruvate, a ratio of phenylalanine to tyrosine, tyrosine, alanine, and / or glucose. The method may comprise determining in the biological sample quantitative values of the following biomarkers: - glycoprotein acetyls, - albumin, - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, and - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may further comprise determining in the biological sample quantitative values of at least one biomarker of the following, or at least two, or at least three, or at least four, or at least five, or at least six, or at least seven, or at least eight, or at least nine, or at least ten, or all of the biomarkers of the following: - at least one of cholesterol in very low density lipoprotein (VLDL), triglycerides in VLDL, or total lipids in VLDL, - total triglycerides, - at least one of cholesterol in low density lipoprotein (LDL), triglycerides in LDL, total lipids in LDL, cholesterol in large low density lipoprotein (L-LDL), triglycerides in L-LDL, or total lipids in L-LDL, - at least one of small high density lipoprotein (S-HDL) particles, total lipids in S-HDL, phospholipids in S- HDL, cholesterol in S-HDL, esterified cholesterol in S-HDL, or free cholesterol in S-HDL, - at least one of large high density lipoprotein (L-HDL) particles, total lipids in L-HDL, phospholipids in L- HDL, cholesterol in L-HDL, esterified cholesterol in L-HDL, and free cholesterol in L-HDL, - oleic acid, - docosapentaenoic acid, - omega-3 fatty acids, - saturated fatty acids, - polyunsaturated fatty acids (PUFAs), - citrate, - acetate, or - lactate. In general, it may be that additional biomarkers may improve the accuracy of the method. However, even a subset of the biomarkers described in this specification may allow for or assist in detecting or assessing the inflammatory state or condition in the canine subject and / or determining whether the canine subject is at risk of developing the inflammatory state or condition. In the context of this specification, the term “glycoprotein acetyls” may refer to the abundance of circulating glycated proteins, and / or to a nuclear magnetic resonance spectroscopy (NMR) signal that represents the abundance of circulating glycated proteins. Glycoprotein acetyls may include signals from a plurality of different glycoproteins, including e.g. alpha-1-acid glycoprotein, alpha-1 antitrypsin, haptoglobin, transferrin, and / or alpha-1 antichymotrypsin. Glycoprotein acetyls and a method for measuring them in humans are described e.g. in Kettunen et al., 2018, Circ Genom Precis Med. 11:e002234 (DOI: 10.1161 / CIRCGEN.118.002234). In the context of this specification, the term “albumin” may be understood as referring to serum albumin (often referred to as blood albumin). It is an albumin found in vertebrate blood. Albumin is a globular, water-soluble serum protein of approximate molecular weight of 65,000 Daltons. The measurement of human albumin using NMR is described e.g., in publications by Kettunen et al., 2012, Nature Genetics 44, 269-276; Soininen et al., 2015, Circulation: Cardiovascular Genetics 8, 212–206 (DOI: 10.1161 / CIRCGENETICS.114.000216). Albumin may also be measured by various other methods, for example by clinical chemistry analyzers. Examples of such methods may include e.g., dye-binding methods such as bromocresol green and bromocresol purple. In the context of this specification, the term “pyruvate” may refer to the pyruvate molecule and / or pyruvic acid. In the context of this specification, the term “phenylalanine” may refer to the phenylalanine amino acid. In the context of this specification, the term “tyrosine” may refer to the tyrosine amino acid. In the context of this specification, the term “alanine” may refer to the alanine amino acid. In the context of this specification, the term “glucose” may refer to the sugar D-glucose. In the context of this specification, the term “lactate” may refer to the lactate molecule or lactic acid. In the context of this specification, the term “citrate” may refer to the citrate molecule and / or citric acid. In the context of this specification, the term “acetate” may refer to the acetate molecule or to acetic acid. In the context of this specification, the term “saturated fatty acids” (SFAs) may refer to total saturated fatty acids. Saturated fatty acids may be or comprise fatty acids which have no double bonds in their structure. Palmitic acid (16:0) and stearic acid (18:0) are examples of abundant SFAs in canine serum. In the context of this specification, the term “polyunsaturated fatty acids” (PUFAs) may refer to total polyunsaturated fatty acids. PUFAs may be understood as being or comprising fatty acids which have two or more double bonds in their structure. The double bonds of PUFAs may separated by a methylene group (=CH-CH2-CH=); conjugated double bonds are normally not present in natural PUFAs. The double bonds are usually in the cis configuration. PUFAs may be divided into omega-3 and omega-6 PUFAs depending on how the double bonds are situated in fatty acids. In the context of this specification, the term “omega-3 fatty acids” may refer to total omega-3 fatty acids, i.e. the total omega-3 fatty acid amounts and / or concentrations, i.e. the sum of different omega-3 fatty acids. Omega-3 fatty acids are polyunsaturated fatty acids. In omega-3 fatty acids, the last double bond in the fatty acid chain is the third bond counting from the methyl end. Omega-3 fatty acids may include fatty acids such as α-linolenic acid and docosahexaenoic acid. In the context of this specification, the term “oleic acid” may refer to the monounsaturated omega-9 fatty acid oleic acid (18:1ω-9). In the context of this specification, the term “docosapentaenoic acid” may refer to the polyunsaturated fatty acid docosapentaenoic acid that has two primary isomers all-cis- 4,7,10,13,16-docosapentaenoic acid and all-cis-7,10,13,16,19- docosapentaenoic acid. In the context of this specification, the term “LDL” refers to low density lipoprotein. In the context of this specification, the term “HDL” refers to high density lipoprotein. In the context of this specification, the term “VLDL” refers to very low density lipoprotein. Each one of these lipoprotein classes may be divided into two or more subfractions (subclasses). It is possible acquire a quantitative value for particles in each subclass. Subclasses can be categorized as small (S-), large (L-), or very large (XL-). It is possible to separate subfractions of the lipoprotein classes e.g. by gel electrophoresis, ion mobility or density-gradient ultracentrifugation. Nuclear magnetic spectroscopy (NMR) may also be used to quantitatively measure spectral signals generated by terminal methyl groups on lipids within lipoprotein particles of individual subfractions. The division into the subfractions may be based on the particle size of the lipoprotein(s). The average diameter for lipoprotein subfraction particles may be calculated as the particle concentration weighted average of the subfraction diameters. The lipoprotein subfractions may be, in some embodiments, determined by NMR. In the context of this specification, the term “cholesterol” refers to an organic sterol compound with the IUPAC name cholest-5-en-3β-ol. It can exist in unmodified from (free cholesterol) or in esterified form (esterified cholesterol). In the context of this specification, the term “free cholesterol”, for example in a lipoprotein subfraction, may be understood as referring to unesterified cholesterol. In the context of this specification, the term “esterified cholesterol”, for example in a lipoprotein subfraction, may be understood as referring to esterified cholesterol, wherein the cholesterol is bonded via an ester bond between a carboxylate group of a fatty acid and a hydroxyl group of the cholesterol. In the context of this specification, the phrase “cholesterol in VLDL” or “cholesterol in LDL”, or cholesterol in any other lipoprotein class or subfraction, may be understood as referring to total cholesterol in said lipoprotein class or subfraction. In the context of this specification, the term “triglycerides in VLDL”, or in any other lipoprotein subfraction, may be understood as referring to total triglycerides in said lipoprotein or subfraction. In the context of this specification, the term “phospholipids”, for example in a lipoprotein subfraction, may be understood as referring to total phospholipids, for example in said lipoprotein subfraction. Phospholipids are polar lipids that include two fatty acids, a glycerol unit, a phosphate group and a glycerol backbone. A panel of isolated biomarkers comprising a plurality of the biomarkers described in this specification is also disclosed. The plurality of the biomarkers may be any plurality, subset, or combination of the biomarkers and / or additional biomarkers described in this specification. The biomarker associations may be similar or, at least to some extent, different for the different inflammatory conditions. Therefore, the same individual biomarkers and combinations of bi- omarkers may be extended to also detect specific inflammatory dis- eases or to assess whether the canine subject is likely to suffer from an acute or chronic inflammatory condition. Examples of such inflammatory conditions or diseases include acute gastroenteritis, pyometra, a neoplastic disease, atopic dermatitis, parodontitis, urinary tract infection, canine infections respiratory disease, and / or osteoarthritis. Furthermore, the biomarker(s) may significantly improve the possibility of identifying the inflammatory state or condition in the canine subject, even when accounting for other methods used to detect inflammation in canine subjects, such as C-reactive pro- tein (CRP). Currently widely used methods, such as acute state protein CRP for detecting inflammation, are unable to detect chronic low-grade inflammation. The biomarker(s) may significantly improve the detection of chronic low-grade inflammation in ca- nines. In an embodiment, the method comprises determining a quantitative value of glycoprotein acetyls. In an embodiment, the method comprises determining a quantitative value of albumin. In an embodiment, the method comprises determining a quantitative value of pyruvate. In an embodiment, the method comprises determining a quantitative value of the ratio of phenylalanine to tyrosine and / or a quantitative value of tyrosine. The phrase “a ratio of phenylalanine to tyrosine and / or tyrosine” may be understood as referring to a ratio of phenylalanine to tyrosine; to tyrosine; or to both tyrosine and the ratio of phenylalanine to tyrosine. In an embodiment, the method comprises determining a quantitative value of phenylalanine. Although the quantitative value of the ratio of phenylalanine to tyrosine may be a particularly well-suited marker in the method of the present disclosure, at least in some embodiments, it may be substituted or supplemented by the quantitative value of tyrosine and / or by the quantitative value of phenylalanine. In an embodiment, the method comprises determining a quantitative value of alanine. In an embodiment, the method comprises determining a quantitative value of glucose. In an embodiment, the method comprises determining a quantitative value of cholesterol in very low density lipoprotein (VLDL). In an embodiment, the method comprises determining a quantitative value of triglycerides in VLDL. In an embodiment, the method comprises determining a quantitative value of total lipids in VLDL. In an embodiment, the method comprises determining a quantitative value of total triglycerides. In an embodiment, the method comprises determining a quantitative value of cholesterol in low density lipoprotein (LDL). In an embodiment, the method comprises determining a quantitative value of triglycerides in LDL. In an embodiment, the method comprises determining a quantitative value of total lipids in LDL. In an embodiment, the method comprises determining a quantitative value of cholesterol in large low density lipoprotein (L-LDL). In an embodiment, the method comprises determining a quantitative value of triglycerides in L-LDL. In an embodiment, the method comprises determining a quantitative value of total lipids in L-LDL. In an embodiment, the method comprises determining a quantitative value of small high density lipoprotein (S-HDL) particles. In an embodiment, the method comprises determining a quantitative value of total lipids in S-HDL. In an embodiment, the method comprises determining a quantitative value of phospholipids in S-HDL. In an embodiment, the method comprises determining a quantitative value of cholesterol in S-HDL. In an embodiment, the method comprises determining a quantitative value of esterified cholesterol in S-HDL. In an embodiment, the method comprises determining a quantitative value of free cholesterol in S-HDL. In an embodiment, the method comprises determining a quantitative value of large high density lipoprotein (L-HDL) particles. In an embodiment, the method comprises determining a quantitative value of total lipids in L-HDL. In an embodiment, the method comprises determining a quantitative value of phospholipids in L-HDL. In an embodiment, the method comprises determining a quantitative value of cholesterol in L-HDL. In an embodiment, the method comprises determining a quantitative value of esterified cholesterol in L-HDL. In an embodiment, the method comprises determining a quantitative value of free cholesterol in L-HDL. In an embodiment, the method comprises determining a quantitative value of oleic acid. In an embodiment, the method comprises determining a quantitative value of docosapentaenoic acid. In an embodiment, the method comprises determining a quantitative value of omega-3 fatty acids. In an embodiment, the method comprises determining a quantitative value of saturated fatty acids. In an embodiment, the method comprises determining a quantitative value of polyunsaturated fatty acids (PUFAs). In an embodiment, the method comprises determining a quantitative value of citrate. In an embodiment, the method comprises determining a quantitative value of acetate. In an embodiment, the method comprises determining a quantitative value of lactate. The metabolic biomarker(s) described in this specification have been found to be significantly different, i.e., their quantitative values (such as amount and / or concentration) have been found to be significantly higher or lower, for canine subjects suffering from an inflammatory state or condition or at risk of developing the inflammatory state or condition. The biomarkers may be detected and quantified from blood, serum, or plasma, or other suitable biological sample, and may be used to detect or assess the inflammatory state or condition and / or to determine whether the canine subject is at risk of developing the inflammatory state or condition, either alone or in combination with other biomarkers. The method may comprise determining in the biological sample quantitative values of a plurality of the biomarkers described in this specification, such as two, three, four, five or six of the biomarkers. For example, the plurality of the biomarkers may comprise 2, 3, 4, 5, or 6 (i.e., at least 2, at least 3, at least 4, at least 5, at least 6) of the biomarkers described in this specification. The term “plurality of the biomarkers” may thus, within this specification, be understood as referring to any number (above one) of the biomarkers. The term “plurality of the biomarkers” may thus be understood as referring to any number (above one) and / or combination or subset of the biomarkers described in this specification. Determining the plurality of the biomarkers may increase the accuracy of the prediction of whether the canine subject is suffering an inflammatory state or condition or at risk of developing the inflammatory state or condition. In general, it may be that the higher the number of the biomarkers, the more accurate or predictive the method. However, even a single biomarker described in this specification may allow for or assist in determining whether the canine is suffering from an inflammatory state or condition. The plurality of the biomarkers may be measured from the same biological sample or from separate biological samples and using the same analytical method or different analytical methods. In an embodiment, the plurality of biomarkers may be a panel of a plurality of biomarkers. In the context of this specification, the term “quantitative value” may refer to any quantitative value characterizing the amount and / or concentration of a biomarker. For example, it may be the amount or concentration of the biomarker in the biological sample, or it may be a signal derived from nuclear magnetic resonance spectroscopy (NMR) or other method suitable for detecting the biomarker in a quantitative manner. Such a signal may be indicative of or may correlate with the amount or concentration of the biomarker. It may also be a quantitative value calculated from one or more signals derived from NMR measurements or from other measurements. Quantitative values may, additionally or alternatively, be measured using a variety of techniques. Such methods may include mass spectrometry (MS), gas chromatography combined with MS, high performance liquid chromatography alone or combined with MS, immunoturbidimetric measurements, ultracentrifugation, ion mobility, enzymatic analyses, colorimetric or fluorometric analyses, immunoblot analysis, immunohistochemical methods (e.g. in situ methods based on antibody detection of metabolites), and immunoassays (e.g. ELISA). Examples of various methods are set out below. The method used to determine the quantitative value(s) in the canine subject should be the same method that is used to determine the quantitative value(s) in a control subject / control subjects or in a control sample / control samples. In the context of this specification, the wording “comparing the quantitative value(s) of the biomarker(s) to a control sample or to a control value(s)” may be understood, as a skilled person would, as referring to comparing the quantitative value or values of the biomarker or biomarkers, to a control sample or to a control value(s) either individually or as a plurality of biomarkers (e.g. when a risk score is calculated from the quantitative values of a plurality of biomarkers), depending e.g. on whether the quantitative value of a single (individual) biomarker or the quantitative values of a plurality of biomarkers are determined. In the contect of this specification, the subject may be a canine subject. In the context of this specification, the term “canine” may be understood as referring to a subject of the species Canis lupus familiaris, i.e. a dog. The canine subject may be healthy or generally healthy or have an existing inflammatory state, disease or condition. The canine subject may have an already existing form of inflammatory state, disease or condition, wherein symptoms of the inflammatory state, disease or condition may include chronic low-grade inflammation. Or, in some embodiments, the canine subject may be suspected of suffering from an inflammatory state or condition, or may display one or more symptoms thereof. In the context of this specification, the term “biomarker” may refer to a biomarker, for example a chemical or molecular marker, that may be found to be associated with or capable of predicting the risk of the inflammatory state or condition. It does not necessarily refer to a biomarker that would be statistically fully validated as having a specific effectiveness in a clinical setting. The biomarker may be a metabolite, a compound, a lipid, a protein, a lipoprotein, a moiety, a functional group, a composition, a combination of two or more metabolites, lipids, proteins, lipoproteins, moieties, functional groups and / or compounds, a (measurable or measured) quantity thereof, a ratio or other value derived thereof, or in principle any measurement reflecting a chemical and / or biological component that may be found associated with the inflammatory state or condition. The biomarkers and any combinations thereof, optionally in combination with further analyses and / or measures, may be used to measure a biological process specific to and / or indicative of the inflammatory state or condition. Any “ratio” of two biomarkers may refer to the ratio of the quantitative values of the two biomarkers, or to a ratio of a quantitative value of a biomarker to another measure (e.g. a quantitative value of a metabolite or compound, or total quantitative value of metabolites or compounds of the same class). In the context of this specification, the term “inflammatory state or condition” may be understood as referring to a state, disease, disorder, and / or condition involving inflammation and / or one or more inflammatory mechanisms. The inflammatory state or condition may affect associated tissue(s), including for instance gums, uterus, and / or muscles, depending on the state or condition. In the inflammatory state, activity of immune cells and other pro-inflammatory factors may result in the biological inflammation response. The inflammatory state or condition may be an acute condition. “Acute” in the context of this specification may be understood as rapid or otherwise short- lasting in its effects. The inflammatory state or condition may be a chronic condition. “Chronic” in the context of this specification may be understood as persistent or otherwise long-lasting in its effects and / or a disease or condition that is reoccurring in nature. In some embodiments, the inflammatory state may be an inflammatory state preceding the onset or full onset of an inflammatory condition, wherein the inflammatory condition may be e.g. acute gastroenteritis, urinary tract infection, canine infectious respiratory disease, atopic dermatitis, parodontitis, osteoarthritis, and / or a neoplastic disease. For example, in such embodiments, the inflammatory state may be an inflammatory state associated with the early onset of the inflammatory condition, or it may be low-grade systemic inflammation or be associated with low-grade systemic inflammation. The inflammatory state or condition may, at least in some embodiments, be low-grade systemic inflammation or be associated with low-grade systemic inflammation. The method may thus be a method for detecting or assessing a low-grade systemic inflammation, or an inflammatory state or condition associated with a low-grade systemic inflammation, in a canine subject. Low- grade systemic inflammation is a pathological state characterized by continuous activation of inflammatory mediators in the absence of overt inflammation. The inflammatory state or condition may, at least in some embodiments, be chronic low-grade systemic inflammation or be associated with chronic low-grade systemic inflammation. The inflammatory condition may comprise or be at least one of pyometra, acute gastroenteritis, urinary tract infection, canine infectious respiratory disease, atopic dermatitis, parodontitis, osteoarthritis, or a neoplastic disease, such as cutaneous neoplasia or noncutaneous neoplasia. However, the underlying cause of the inflammatory state or condition is not particularly limited, and various other causes or conditions may be contemplated. The inflammatory condition may comprise or be at least one of pyometra, acute gastroenteritis, urinary tract infection, canine infectious respiratory disease, atopic dermatitis, parodontitis, or a neoplastic disease, such as cutaneous neoplasia or noncutaneous neoplasia. The inflammatory condition may comprise or be at least one of pyometra, acute gastroenteritis, urinary tract infection, or canine infectious respiratory disease. The inflammatory condition may comprise or be at least one of atopic dermatitis, parodontitis, osteoarthritis, or a neoplastic disease, such as cutaneous neoplasia or noncutaneous neoplasia. The inflammatory condition may comprise or be at least one of atopic dermatitis, parodontitis, or a neoplastic disease, such as cutaneous neoplasia or noncutaneous neoplasia. The inflammatory condition may comprise or be pyometra. The term “pyometra” may be understood as referring to an inflammatory disease, disorder or condition affecting the uterine tissues. Pyometra may be understood as an acute or chronic suppurative infection including accumulation of pus in the uterine lumen. Pyometra may occur in middle-aged to aged unspayed female canine subjects in particular. The inflammatory condition may comprise or be acute gastroenteritis. The term “acute gastroenteritis” may be understood as referring to an inflammatory state or condition of the alimentary tract, i.e. the stomach and / or the intestinal track, of the canine subject. Acute gastroenteritis may be due to a wide variety of underlying causes, including dietary indiscretion, an infectious organism, a toxin, immune dysregulation, and / or a metabolic disorder. The inflammatory condition may comprise or be urinary tract infection. The term “urinary tract infection” may be understood as referring to an infection, such as a bacterial infection, affecting the urinary tract. The inflammatory condition may comprise or be canine infectious respiratory disease. The term “canine infectious respiratory disease” may be understood as referring to a infectious disease, disorder or condition affecting respiratory tissues of a canine subject, which may be caused by various different bacterial and / or viral pathogens. Canine infectious respiratory disease may also be referred to as canine infectious respiratory disease complex (CIRDC) or kennel cough. The inflammatory condition may comprise or be atopic dermatitis. The term “atopic dermatitis” may be understood as referring to an inflammatory and pruritic allergic skin disease in the canine subject. Atopic dermatitis may be a genetically predisposed disease. It may be commonly associated with IgE antibodies to environmental allergens. Atopic dermatitis may also be referred to as canine atopic dermatitis. The inflammatory condition may comprise or be parodontitis. The term “parodontitis” may be understood as referring to an inflammatory disease, disorder or condition affecting the periodontium. It may also be referred to as periodontitis. In parodontitis, the tissues that support the tooth become inflamed, which may ultimately result in attachment loss due to the destruction of the parodontal ligament, cementum and alveolar bone. The inflammatory condition may comprise or be osteoarthritis. The term “osteoarthritis” may be understood as referring a chronic degenerative disease of joint cartilage. The inflammatory condition may comprise or be a neoplastic disease. The neoplastic disease may comprise or be cutaneous neoplasia or noncutaneous neoplasia. The term “neoplasia” may be understood as uncontrolled, abnormal growth of cells and / or a tissue in the canine subject. The neoplasia may be benign or malignant. “Cutaneous neoplasia” may be understood as an abnormal growth of the cells of the skin. “Noncutaneous neoplasia” may, in the context of this specification, be understood as an abnormal growth of cells of a tissue other than skin. The method may further comprise detecting or assessing the inflammatory state or condition in the canine subject and / or determining whether the canine subject is at risk of developing the inflammatory state or condition using a diagnostic score, likelihood, risk score, hazard ratio, odds ratio, and / or predicted absolute risk or relative risk calculated on the basis of at least the quantitative values of the biomarkers. For example, the inflammatory state or condition in the canine subject or the risk of developing thereof may be detected or assessed by creating and / or using a logistic regression model based on quantitative values of the biomarkers. The logistic regression model may be used to determine a diagnostic score or likelihood of the inflammatory state or condition or of the risk thereof for the canine subject. The diagnostic score, likelihood, risk score, hazard ratio, odds ratio, and / or predicted absolute risk or relative risk may be calculated e.g. as shown in the Examples below. For example, the quantitative values of the biomarkers (or the plurality of biomarkers) may be measured using a suitable method, for example with NMR spectroscopy, and combined using regression algorithms (for example, logistic regression), multivariate analyses and / or using machine learning analysis. Before such calculations, e.g. regression analysis, multivariate analysis or machine learning, any missing values in the biomarkers may be imputed e.g. with random forest imputation. A number of biomarkers, for example two, three, four, five, six, or more, that may be considered most associated with the inflammatory state or condition may be selected for use in the prediction model. An increase or a decrease in the risk score, hazard ratio, and / or predicted absolute risk and / or relative risk may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The risk score and / or hazard ratio and / or predicted absolute risk or relative risk may be calculated based on any plurality, combination or subset of biomarkers described in this specification. The risk score may be calculated e.g. as a weighted sum of individual biomarkers, i.e. a plurality of the biomarkers. The weighted sum may be e.g. in the form of a multi-biomarker score defined as ∑i〖βi*ci〗+ β0; where i is the index of summation over individual biomarkers, is the weighted coefficient attributed to biomarker i, ciis the blood concentration of biomarker i, and β0is an intercept term. For example, the risk score can be defined as: β1*concentration(glycoprotein acetyls) + β2* concentration(monounsaturated fatty acid ratio to total fatty acids) + β3* concentration(albumin) + where β1,β2,β3are multipliers for each biomarker according to the association magnitude with the inflammatory state or condition and β0 is an intercept term. As a skilled person will understand, the biomarkers mentioned in this example may be replaced by any other biomarker(s) described in this specification. In general, the more biomarkers are included in the risk score, the stronger the predictive performance may become. When additional biomarkers are included in the risk score, the weights may change for all biomarkers according to the optimal combination for detecting or assessing the inflammatory state or condition and / or for determining whether the canine subject is at risk of developing the inflammatory state or condition. The diagnostic score, likelihood, risk score, hazard ratio, odds ratio, and / or predicted absolute risk or relative risk may be calculated based on at least one further measure, for example a characteristic of the canine subject. Such a characteristic may be determined prior to, simultaneously, or after the biological sample is obtained from the subject. As a skilled person will understand, some of the characteristics may be information, e.g. clinical data, collected earlier. Some of the characteristics may be determined (or may have been determined) by biochemical or clinical diagnostic measurements and / or medical diagnosis. Such characteristics could include, for example, age, sex, and / or sterilization status. Such at least further measure or characteristics may, additionally or alternatively, include C- reactive protein (CRP) or a quantitative value thereof. The quantitative value, or the initial quantitative value, of the biomarkers, or the plurality of the biomarkers, may be measured using nuclear magnetic resonance (NMR) spectroscopy, for example proton NMR (1H-NMR). NMR spectroscopy may provide a particularly efficient and fast way to measure biomarkers, including many biomarkers simultaneously, and can provide quantitative values for them. NMR spectroscopy also typically requires very little sample pre-treatment or preparation. The biomarkers measured with NMR spectroscopy can effectively be measured for a large number of samples such as blood (serum or plasma) using an NMR metabolomics assay previously published by Ottka et al., 2021, Veterinary Clinical Pathology, 50, 410–426, (DOI: 10.1111 / vcp.12954). The method described in Ottka et al., 2021 allows for the measurement of potential 123 biomarkers in canines. Thus the quantitative values of the biomarkers may be measured using nuclear magnetic resonance (NMR) spectroscopy. However, quantitative values for various biomarkers described in this specification may also be performed by techniques other than NMR. For example, mass spectrometry (MS), enzymatic methods, antibody-based detection methods, or other biochemical or chemical methods may be contemplated, depending on the biomarker. For example, glycoprotein acetyls can be measured or approximated by immunoturbidimetric measurements of alpha-1-acid glycoprotein, haptoglobin, alpha-1-antitrypsin, and transferrin (e.g., as described in Ritchie et al., 2015, Cell Syst. 28;1(4):293-301). Triglycerides and cholesterol in lipoproteins or fractions thereof, such as VLDL, can be quantified using ultracentrifugation and by high performance liquid chromatography. Triglycerides, cholesterol, free cholesterol, esterified cholesterol, phospholipids, and total lipids in various lipoprotein classes and subfractions can be quantified using high performance liquid chromatography. Total triglycerides can be quantified by enzymatic clinical chemistry analysers, such as ROCHE COBAS 6000. In the context of this specification, the term “sample” or “biological sample” may refer to any biological sample obtained from a canine subject or a group or population of canine subjects. The sample may be fresh, frozen, or dry. The biological sample may be a blood sample or a tissue sample. The biological sample may comprise or be, for example, a blood sample, a plasma sample, a serum sample, or a sample derived therefrom. The biological sample may be, for example, a fasting blood sample, a fasting plasma sample, a fasting serum sample, or a fraction obtainable therefrom. However, the biological sample does not necessarily have to be a fasting sample. The blood sample may be a venous blood sample. Some of the biomarkers may be determined from body fluids other than blood or fractions obtainable therefrom. The biological sample may, additionally or alternatively, comprise or be a sample or one or more other body fluids or biofluids, for example an amniotic fluid sample, a urine sample, a saliva sample, a bile sample, a tear sample and / or a spinal fluid sample. The blood sample may be a dry blood sample. The dry blood sample may be a dried whole blood sample, a dried plasma sample, a dried serum sample, or a dried sample derived therefrom. The biological sample may be obtained from the subject prior to determining the quantitative values of the biomarkers. The method may thus comprise obtaining the biological sample from the subject prior to determining the quantitative values of the biomarkers. Taking a blood sample or a tissue sample of a subject or patient is a part of normal clinical practice. The collected blood or tissue sample can be prepared, and serum or plasma can be separated using techniques well known to a skilled person. Methods for separating one or more fractions from biological samples, such as blood samples or tissue samples, are also available to a skilled person. The term “fraction” may, in the context of this specification, also refer to a portion or a component of the biological sample separated according to one or more physical properties, for instance solubility, hydrophilicity or hydrophobicity, density, or molecular size. In the context of this specification, the term “control sample” may refer to a sample obtained from a canine subject known not to suffer from the inflammatory state or condition. The control sample may be matched. In an embodiment, the control sample may be a biological sample from a healthy individual or a generalized population of healthy individuals. The term “control value” may be understood as a value obtainable from the control sample and / or a quantitative value derivable therefrom. An increase or a decrease in the quantitative value(s) of the biomarkers, or the plurality of the biomarkers, when compared to the control sample or to the control value, may be indicative of the canine subject having the inflammatory state or condition and / or of the canine subject having an increased risk of developing the inflammatory state or condition. Whether an increase or a decrease is indicative of the canine subject having an increased risk of developing the disease or condition and / or of the canine subject having an increased risk of developing the inflammatory state or condition, may depend on the biomarker. For example, a 1.2-fold, 1.5-fold, or 2-fold, or 3-fold, increase or a decrease in the quantitative value(s) of the biomarker(s) (or in an individual biomarker of the plurality of the biomarkers) when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of glycoprotein acetyls, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of albumin, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of pyruvate, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the ratio of phenylalanine to tyrosine, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of tyrosine, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of phenylalanine, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of alanine, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of glucose, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of cholesterol in very-low-density lipoprotein (VLDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of triglycerides in very-low-density lipoprotein (VLDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of total lipids in very-low-density lipoprotein (VLDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of cholesterol in low-density lipoprotein (LDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of total lipids in low-density lipoprotein (LDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of cholesterol in large low-density lipoprotein (L-LDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of total lipids in large low-density lipoprotein (L-LDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of small high density lipoprotein particles (S-HDL) when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of total lipids in small high-density lipoprotein (S-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of phospholipids in small high-density lipoprotein (S-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of cholesterol in small high-density lipoprotein (S-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of esterified cholesterol in small high-density lipoprotein (S-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of free cholesterol in small high-density lipoprotein (S-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of large high density lipoprotein particles (L-HDL) when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of total lipids in large high-density lipoprotein (L-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of phospholipids in large high-density lipoprotein (L-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of cholesterol in large high-density lipoprotein (L-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of esterified cholesterol in large high-density lipoprotein (L-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of free cholesterol in large high-density lipoprotein (L-HDL), when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of oleic acid, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, a decrease in the quantitative value of omega-3 fatty acids, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. In an embodiment, an increase in the quantitative value of saturated fatty acids, when compared to the control sample or to the control value, may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. An increase in the quantitative value of pyruvate, a decrease in the quantitative value of alanine, an increase in the quantitative value of phenylalanine, an increase in the phenylalanine to tyrosine ratio, a decrease in the quantitative value of tyrosine, an increase in the quantitative value of glucose, an increase in the quantitative value of glycoprotein acetyls, and / or a decrease in the quantitative value of albumin, when compared to the control sample or to the control value(s), may be indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The biomarker associations may be similar or, to some extent, different for different inflammatory conditions. Therefore, depending on the biomarkers determined, it may be possible to determine or assess that the canine subject is suffering from an inflammatory state or condition in general, and / or at risk of developing the inflammatory state or condition in general; or it may be possible to determine or assess which inflammatory condition(s) the canine subject is likely to suffer from and / or at risk of developing. Biomarkers indicative of the inflammatory state or condition or the risk thereof in the canine subject could in some embodiments help to enable more effective screening and better targeted preventative treatment. In some embodiments, the method may further comprise de- termining in the biological sample quantitative value(s) of phe- nylalanine and / or the ratio of phenylalanine to tyrosine, and the inflammatory condition may comprise or be at least one of pyometra, acute gastroenteritis, urinary tract infection, or canine infec- tious respiratory disease. An increase in the quantitative value of phenylalanine and / or in the the quantitative value of the ratio of phenylalanine to tyrosine may be indicative of the inflammatory state or condition in the canine subject, such as pyometra, acute gastroenteritis, urinary tract infection, and / or canine infectious respiratory disease, and / or of the canine subject having an in- creased risk of developing the inflammatory state or condition. The method may further comprise determining in the biological sample quantitative value(s) of - at least one of cholesterol in very low density lipoprotein (VLDL), triglycerides in VLDL, or total lipids in VLDL, and / or - total triglycerides. In such an embodiment, the inflammatory state or condi- tion may comprise or be osteoarthritis, parodontitis, a neoplastic disease, pyometra, atopic dermatitis, acute gastroenteritis, and / or canine infectious respiratory disease. In such an embodi- ment, the inflammatory state or condition may comprise or be os- teoarthritis, parodontitis, a neoplastic disease, and / or pyometra. In such an embodiment, the inflammatory state or condition may comprise or be atopic dermatitis, acute gastroenteritis, and / or canine infectious respiratory disease. An increase in the quantitative value(s) of cholesterol in very low density lipoprotein (VLDL), triglycerides in VLDL, total lipids in VLDL and / or total triglycerides may be indicative of the inflammatory state or condition, such as osteoarthritis, parodontitis, a neoplastic disease, and / or pyometra, in the canine subject, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. A decrease in the quantitative value(s) of cholesterol in very low density lipoprotein (VLDL), triglycerides in VLDL, total lipids in VLDL and / or total triglycerides may be indicative of the inflammatory state or condition, such as atopic dermatitis, acute gastroenteritis, and / or canine infectious respiratory disease, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may further comprise determining in the bio- logical sample quantitative value(s) of - at least one of cholesterol in low density lipoprotein (LDL), triglycerides in LDL, total lipids in LDL, cho- lesterol in large low density lipoprotein (L-LDL), tri- glycerides in L-LDL, or total lipids in L-LDL. In such an embodiment, the inflammatory state or condi- tion may comprise or be osteoarthritis, pyometra, and / or noncuta- neous neoplasia. An increase in the quantitative value(s) of cho- lesterol in low density lipoprotein (LDL), triglycerides in LDL, total lipids in LDL, cholesterol in large low density lipoprotein (L-LDL), triglycerides in L-LDL, and / or total lipids in L-LDL may be indicative of the inflammatory state or condition, such as osteoarthritis, pyometra, and / or noncutaneous neoplasia, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may further comprise determining in the bio- logical sample quantitative value(s) of - at least one of small high density lipoprotein (S-HDL) particles, total lipids in S-HDL, phospholipids in S- HDL, cholesterol in S-HDL, esterified cholesterol in S-HDL, or free cholesterol in S-HDL, and / or - at least one of large high density lipoprotein (L-HDL) particles, total lipids in L-HDL, phospholipids in L- HDL, cholesterol in L-HDL, esterified cholesterol in L-HDL, and free cholesterol in L-HDL. In such an embodiment, the inflammatory state or condition may comprise or be atopic dermatitis, acute gastroenteritis, canine infectious respiratory disease, noncutaneous neoplasia, and / or pyometra. A decrease in the quantitative value(s) of at least one of small high density lipoprotein (S-HDL) particles, total lipids in S-HDL, phospholipids in S-HDL, cholesterol in S-HDL, esterified cholesterol in S-HDL, or free cholesterol in S-HDL, at least one of large high density lipoprotein (L-HDL) particles, total lipids in L-HDL, phospholipids in L-HDL, cholesterol in L-HDL, esterified cholesterol in L-HDL, and / or free cholesterol in L-HDL may be indicative of the inflammatory state or condition, such as atopic dermatitis, acute gastroenteritis, canine infectious respiratory disease, noncutaneous neoplasia, and / or pyometra, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may further comprise determining in the bio- logical sample quantitative value(s) of - oleic acid, - docosapentaenoic acid, - omega-3 fatty acids, and / or - saturated fatty acids. In such an embodiment, the inflammatory state or condition may comprise or be pyometra. A decrease in the quantitative value(s) of oleic acid, docosapentaenoic acid, and / or omega-3 fatty acids may be indicative of the inflammatory state or condition, such as pyometra, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may further comprise determining in the bio- logical sample quantitative value of polyunsaturated fatty acids (PUFAs). In such an embodiment, the inflammatory state or condi- tion may comprise or be parodontitis, pyometra, cutaneous neo- plasia, and / or urinary tract infection. A decrease in the quanti- tative value of polyunsaturated fatty acids (PUFAs) may be indic- ative of the inflammatory state or condition, such as parodontitis, pyometra, cutaneous neoplasia, and / or urinary tract infection, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may further comprise determining in the bio- logical sample quantitative value(s) of - citrate, and / or - acetate. In such an embodiment, the inflammatory state or condi- tion may comprise or be atopic dermatitis, acute gastroenteritis, and / or canine infectious respiratory disease. A decrease in the quantitative value(s) of citrate and / or acetate may be indicative of the inflammatory state or condition, such as atopic dermatitis, acute gastroenteritis, and / or canine infectious respiratory dis- ease, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The method may comprise determining in the biological sample quantitative value of glucose. In such an embodiment, the inflammatory state or condi- tion may comprise or be parodontitis and / or pyometra. An increase in the quantitative value of glucose may be indicative of the inflammatory state or condition, such as parodontitis and / or pyom- etra, and / or of the canine subject having an increased risk of developing the inflammatory state or condition. The detection and / or assessment of the inflammatory state or condition, and / or determining whether the canine subject is at risk of developing the inflammatory state or condition, may be used to guide treatment and other efforts for improving the inflammatory state or condition. The method may further comprise administering a treatment to the canine subject to thereby treat the canine subject in order to treat and / or prevent the disease or condition in the canine subject. For example, the treatment may comprise administering to the canine subject or subjecting the canine subject to an antimicrobial agent, an anti-inflammatory agent or treatment, surgical treatment, removal of inflammatory material, pain medication, supportive treatment, and / or a disease- specific treatment. For example, when the inflammatory condition comprises or is acute gastroenteritis, the treatment may include e.g. an anti- microbial agent; an antiemetic agent; a gastroprotectant agent; an antidiarrheal agent; and / or an antiparasitic agent. The supportive treatment may include or be fluid therapy; pain medication; nu- tritional support; dietary management; electrolyte supplementa- tion; and / or probiotics. For example, when the inflammatory condition comprises or is pyometra, the treatment may include e.g. surgical treatment with appropriate pain management, supportive treatment and anti- biotics, and / or removal of inflammatory material using a drug ca- pable of causing contraction of the uterine musculature, and an antibiotic agent, in conjunction with pain management and support- ive treatment. For example, when the inflammatory condition comprises or is neoplasia, the treatment may include e.g. surgical treatment; a disease-specific treatment, such as chemotherapy, radiation therapy, and / or immunotherapy; supportive treatment; pain manage- ment; and / or anti-inflammatory treatment. For example, when the inflammatory condition comprises or is atopic dermatitis, the treatment may include e.g. local or systemic anti-inflammatory treatment using for example a gluco- corticoid, an antihistamine, a calcineurin inhibitor, a JAK in- hibitor, or antibody treatment; a local or systemic antimicrobial agent; immunotherapy; and / or supportive treatment. For example, when the inflammatory condition comprises or is parodontitis, the treatment may include e.g. removal of inflam- matory material using professional periodontal procedures; pre- vention of the formation of parodontitis using dental hygiene homecare products, such as tooth brushing, a specific diet, and / or a chew toy; and / or periodontal surgical treatment. For example, when the inflammatory condition comprises or is urinary tract infection, the treatment may include e.g. an antibiotic; pain medication; and / or supportive treatment, such as a dietary change and / or fluid therapy. For example, when the inflammatory condition comprises or is canine infectious respiratory disease, the treatment may in- clude e.g. supportive treatment, including a cough suppressant, fluid therapy, nutritional support, oxygen therapy, nebulization, and / or coupage; and / or an antibiotic. For example, when the inflammatory condition comprises or is osteoarthritis, the treatment may include e.g. pain medication; anti-inflammatory treatment; surgical treatment; and / or supportive treatment, including dietary therapy and / or a dietary supplement. EXAMPLES Reference will now be made in detail to various embodiments, an example of which is illustrated in the accompanying drawings. The description below discloses some embodiments in such a detail that a person skilled in the art is able to utilize the embodiments based on the disclosure. Not all steps or features of the embodiments are discussed in detail, as many of the steps or features will be obvious for the person skilled in the art based on this specification. EXAMPLE 1 Biomarker measures quantified by nuclear magnetic resonance (NMR) were investigated as to whether metabolic biomarkers could be used to diagnose an inflammatory state or condition in the subject and whether different inflammatory diseases including common canine diseases are marked by different metabolic biomarkers. Study population Dogs were recruited to the study at routine patient visits conducted in the benefit of the patient at four Finnish small animal hospitals, Eläinsairaala Evidensia Tammisto, Vantaa, Finland, Eläinsairaala Mevet, Helsinki, Finland, Lahden Eläinlääkäriasema, Lahti, Finland and Espoon Eläinsairaala, Espoo, Finland between 05 / 2022 and 04 / 2023. 209 healthy control dogs and 226 dogs currently suffering from one of the following conditions were enrolled: acute gastroenteritis, pyometra, cutaneous neoplasia, noncutaneous neoplasia, atopic dermatitis, parodontitis, urinary tract infection, canine infectious respiratory disease, and osteoarthritis. Diagnoses were based on clinical signs, history, and diagnostic tests based on the needs of the individual patient and the attending clinician’s evaluation. In neoplastic diseases, some dogs were already enrolled at suspicion when a suspected tumor was evident, and the diagnosis later confirmed using histology. Inclusion criteria for the control group consisted of at least physical examination conducted within the last 3 months and history not suggestive of disease. In-house CRP analysis (Catalyst Dx CRP, IDEXX Laboratories, Inc., Maine, US) was conducted for all study participants. For control dogs, an in-house clinical chemistry analysis using Catalyst Dx Chem15 (IDEXX Laboratories, Inc., Maine, US) and in-house hematology analysis using ProCyte (IDEXX Laboratories, Inc., Maine, US), were performed additionally to confirm health status. For metabolomics analysis, 2 ml of venous blood was collected by cephalic venipuncture from all dogs. The sample tube type used for sample collection was Vacuette LH Lithium Heparin PREMIUM 2 ml. Heparin plasma (110-500 µl) was separated according to sample tube manufacturer’s recommendations and transferred into cryo tubes (Sarstedt 72.730.005). The samples were stored at refrigerated temperature for maximally one week at the clinic and transported to the NMR metabolomics laboratory by a courier service with chilled using cool packs. The transport duration was under 2 hours. The samples were collected into batches and stored in -80°C for a maximum of 5 months prior to proton nuclear magnetic resonance (1H NMR) spectroscopy analysis provided by PetMeta Labs Ltd, Helsinki, Finland. Ethical approval The blood sample collection from privately owned pet dogs approved in the project license from the Animal Ethical Committee of the County Administrative Board for Southern Finland (ESAVI / 16933 / 2021). Whenever possible, blood sampling was conducted from the same needle prick used for routine laboratory diagnostics conducted for the benefit of the patient. The dog owners approved and signed an informed consent form during study inclusion and could withdraw from the study at any point. The dogs’ clinical information was gathered in a pseudonymized form and no owner information was gathered in the study. 1H NMR spectroscopy A targeted NMR spectroscopy method optimized and validated for dogs was utilized for metabolomics analyses. The method has been thoroughly described in Ottka et al., 2021, Veterinary Clinical Pathology, 50, 410–426, (DOI: 10.1111 / vcp.12954). The method is highly automated from sample processing to the processing of the NMR spectra. The used equipment include a sample changer SampleJet (Bruker Corp., Billerica, Massachusetts, USA), a PerkinElmer JANUS Automated Workstation with an 8-tip dispense arm with Varispan (PerkinElmer Inc., Waltham, Massachusetts, USA) and a Bruker AVANCE III HD 500 NMR spectrometer with a 5 mm triple-channel (1H, 13C, 15N) z-gradient Prodigy probe head. Sample preparation includes light mixing of the sample and removal of possible precipitate by centrifugation, sample transfer to individual NMR tubes and mixing with sodium phosphate buffer. Metabolite quantification in absolute units is achieved by processing the NMR spectra using scripts optimized for canine samples with quantification based on regression modeling. The pro- prietary software utilized has integrated quality control. Metab- olite quantification and the proprietary software utilized is de- scribed in more detail in Würtz et al., 2017, American Journal of Epidemiology, 186, 9, 11084–1096, (DOI: 10.1093 / aje / kwx016). In short, Bayesian regression modelling was used to convert the spec- tral information to absolute concentrations of the metabolic measures. The method quantitates 123 measurands, including a de- tailed lipoprotein analysis, amino acids, fatty acids, triglycer- ides, cholesterols, glycolysis-related metabolites, albumin, cre- atinine and GlycA. Data pre-processing Statistical analyses and data preprocessing were con- ducted in R programming environment (R Core Team, 2023) and Mi- crosoft Office Excel (Microsoft Corporation, Redmond, WA, US). The laboratory and health data of all dogs was carefully reviewed and evaluated. Dogs were removed from the control group, if CRP, clinical chemistry or hematology were suggestive of a subclinical disease or if health data was suggestive of a disease. Control group dogs that had one or several blood test parameters outside reference intervals but whose results were not directly suggestive of disease, were flagged to be later removed from anal- yses if they would have outlier results in metabolomics results. The disease status of case group dogs was also carefully reviewed, and dogs with unclear diagnoses, misdiagnoses or in the neoplasia groups, dogs that were later reported not to have histological evidence of neoplasia, were removed. The metabolomics data was carefully reviewed. Dogs with all or most metabolite values missing were removed from the da- taset. Each metabolite was assessed for missing observations and metabolites with over 20% missing values were removed from the dataset. Three such measurands were identified and removed: very large very low-density lipoprotein phospholipids (XL-VLDL PL), very large very low-density lipoprotein esterified cholesterol (XL-VLDL CE) and very large very low-density lipoprotein free cho- lesterol (XL-VLDL FC), which typically have very low values in healthy dogs. The data was also evaluated for outliers, and when outlying results were observed in control group dogs, their health and laboratory data were once again carefully evaluated and dogs removed if any meaningful changes were observed, including previ- ously detected laboratory value changes that were not directly suggestive of disease on their own and moderate changes in muscle or body condition. These steps resulted a final control group of 175 dogs and a case group of 207 dogs with one of the studied diseases. The remaining missing observations in metabolite results were imputed using Random Forest imputation using the packages missForest version 1.5, dplyr version 1.1.2 and tidyverse. The out-of-bag error of the imputation was 0.136, indicating suffi- cient performance. A different approach for missing value imputation was used for CRP values. CRP values below the detection limit were imputed by of the detection limit. CRP values over the detection limit were imputed by detection limit +10 mg / L. One dog in the pyometra group did not have CRP measured. This dog was removed from analyses regarding CRP and the performance of CRP against the metabolic measures. Statistical analyses Descriptive statistics were calculated to characterize the study groups. The normality of the variables was assessed using the Shapiro-Wilk test using the package stats. Since most metabo- lites were not normally distributed, non-parametric statistical methods were used in the statistical analyses. First, the general changes observed in all studied dis- eases were examined by comparing the case group comprising of all dogs with one of the studied diseases with the healthy control group. Before evaluating the metabolic differences between these groups, it was checked whether there were significant differences in the physiological characteristics in these groups that should be accounted for in further statistical analyses. This was done using the Wilcoxon rank sum test using the package stats. No sig- nificant differences in sex, sterilization status or fasting du- ration were observed, but a significant difference was observed in age between the two groups. Since an age difference was observed and age can significantly affect metabolite concentrations, age was taken into account in further statistical analyses. It was also tested whether the medication status (whether or not some sort of medication was used in the last 24 hours leading to sample collection) affected metabolite concentrations in the diseased dogs. Medication status did not significantly affect metabolite concentrations. The concentrations of each metabolite between the case and control groups were compared using logistic regression using the package stats. Individual logistic regression models were cre- ated for each metabolite. To account for the possible effects of differing ages between cases and controls, all models were adjusted for age. The changes were visualized by creating a bubble plot using the package bubbleHeatmap version 0.1.1 with the help of the package grid. The bubble plot was based on the logistic regression p-values and the median absolute deviation (MAD) difference of both groups from the median of the full dataset. To see whether age affected the interpretation of dif- ferences in metabolite concentrations between the case and control group and to see whether the metabolite associations were robust using a different statistical method, the Wilcoxon rank sum test using the package stats was used to test the differences between the case and control group. The results were highly similar to those of logistic regression adjusted for age, indicating that changes in metabolite concentrations were observed independently from age. To determine which metabolites best classified the dogs in the case and control groups, a multivariate logistic regression model adjusted by age was created using the package stats. Only metabolites with p-values under 0.05 in univariate logistic re- gression were used in model creation. Model building was conducted using forward stepwise selection based on p-values. In each step, the metabolic measure with the lowest p-value was added to the model and the process repeated until no further metabolites reached statistical significance. The Akaike information criterion (AIC) of each model was also evaluated and AIC-based forward stepwise selection would have resulted in the same metabolites selected into the model in each step. The final model included 6 metabolic measures: albumin, alanine, pyruvate, GlycA, glucose, and the ra- tio of phenylalanine to tyrosine. It was carefully evaluated whether the final model met the logistic regression assumptions and whether it was appropriately fitted. The presence of multicollinearity was tested by evaluating variance inflation factors (VIF) using the package car. The VIF:s of all variables ranged from 1.07 to 1.58, indi- cating no multicollinearity. The presence of influential observa- tions was checked by plotting the standardized residuals using the package broom. All observations had standardized residuals below 2.6, indicating no influential outlier observations. The assump- tion of the linearity of the logit was tested by adding an inter- action term between each variable and its natural logarithm to the model. None of these interaction terms reached statistical sig- nificance (p < 0.05), indicating that the linearity of the logit assumption is held. The performance of the model was tested using 10-fold cross validation using the package cvAUC version 1.1.4. The used performance metric was the 10-fold cross-validated area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Secondly, the metabolic changes caused by each individual disease were investigated. If a dog consecutively had two of the studied conditions, it was included in both groups. Again, it was first evaluated whether there were significant differences in the physiological characteristics in these groups using the Wilcoxon rank sum test using the package stats. These results are shown in Table 1. In addition to differences in age, some groups had dif- ferent amounts of dogs with each sex or sterilation status than the control group. However, since many of the studied groups were small, adjusting the logistic regression models for all these physiological factors would have been statistically questionable. Since sex and sterilation status have only very minor effects on the metabolic measures tested, it was decided to only adjust the models for age. It was also tested whether the medication affected metabolite concentrations in each of the diseases. The effects of medications on metabolite concentrations were insignificant in all groups. The concentrations of each metabolite between the control group and each separate disease were again compared using indi- vidual logistic regression models for each metabolic measure ad- justed by age using the package stats. The changes were visualized by creating a bubble plot using the package bubbleHeatmap version 0.1.1 with the help of the package grid. The bubble plot was based on the logistic regression p-values and the control group MAD difference of from the median of the control group. Since the groups were rather small for certain diseases, another statistical method was utilized to check whether the re- sults were similar to those of logistic regression. Thus, the differences in metabolite concentrations between each group and the control group were tested using the Wilcoxon rank sum test using the package stats. The results were very similar to those of logistic regression with only minor differences in result inter- pretation. Thirdly, the ability of CRP and the metabolic measures to detect each of the studied diseases was compared. The classifica- tion ability of CRP, GlycA, albumin, the combination of GlycA and albumin and the final multivariate logistic regression model cre- ated earlier and based on the metabolic measures, were tested using logistic regression using the package stats and the performance evaluated using AUC values using the package pROC. The ROC curves of the final multivariate metabolite model in each disease were plotted using the same package. All plots created in this study were finalized in Inkscape. Group demography The demographical information of the study groups is pre- sented in Table 1. Dogs suffering from pyometra, neoplastic dis- eases, parodontitis and arthritis were significantly older than the control group, whereas dogs with canine infectious respiratory disease were younger. All groups consisted of a wide range of different breeds, as well as mixed breed dogs. The most frequently observed type of noncutaneous neoplasia in the data were different types of mammary tumors (n = 9), lymphoma (n = 8), leukemia (n = 3), and mast cell tumors (n = 3). The most common forms of cutaneous neoplasia in the data were cutaneous mast cell tumors (n = 9) and lipoma (n = 7). Table 1: Clinical characteristics of study participants. The symbol * indicated significant difference from the control group. Group Disease N Age me- %male / % N state dian female steri- breeds (range) lized Con- Healthy 175 5.6 (1.0 36 / 64 33 64 trol - 16.4) Case Case 207 8 (0.3 - 44 / 56 42 90 group to- 16.5)* tal Acute 29 5.6 (0.6 34 / 66 48 24 gastroen- – 12.7) teritis Pyometra 14 8.4 (4.9 0 / 100* 0* 13 – 11.3)* Cutaneous 27 9.2 (2.4 48 / 52 52 19 neoplasia – 16.1)* Noncuta- 41 10.3 44 / 56 52 16 neous ne- (3.5 – oplasia 14.4)* Atopic 27 4.5 (1.6 61 / 39* 50 20 dermati- – 11.4) tis Parodon- 33 9.5 (3.3 55 / 45* 45 25 titis – 16.5)* Urinary 19 7.8 (0.3 37 / 63 37 14 tract in- – 14.7) fection Canine 16 2.0 (0.4 50 / 50 6* 14 infec- – 7.4)* tious respira- tory dis- ease Arthritis 21 9.9 (3.3 48 / 52 71* 15 – 16.5)* Metabolite association with overall disease occurrence Several metabolites were significantly associated with overall disease occurrence when comparing the healthy control group to the combined case group comprising of all individuals from one of the studied conditions (Figure 1). Figure 1 shows the metabolites associated with all stud- ied inflammatory diseases. The case group includes all dogs suf- fering from one or more of the studied diseases. negLog10p: abso- lute value of the log10 transformed logistic regression p-value adjusted for age, MAD from median: the difference of the group median from the median of the full dataset as median absolute deviations of the full dataset. Abbreviations: FA: fatty acids, C: cholesterol, GlycA: glycoprotein acetyls, Ala: alanine, Gln: glu- tamine, Gly: glycine, His: histidine, BCAA: branched-chain amino acids, Ile: isoleucine, Leu: leucine, Val: valine, Phe: phenylal- anine, Tyr: tyrosine, TG: triglycerides, VLDL: very low-density lipoprotein, LDL: low-density lipoprotein, HDL: high-density lip- oprotein, L: lipids, P: particles, PL: phospholipids, CE: esteri- fied cholesterol, FC: free cholesterol, XL-: very large, L-: large, S-: small. The inflammatory markers GlycA and albumin had ones of the strongest associations with overall disease burden, GlycA be- ing elevated in the case group and albumin decreased as a negative inflammatory marker. Several amino acid changes were observed in the case group, the largest of these being decreased tyrosine and glycine, and an increased ratio of phenylalanine to tyrosine. Of sugar metabolism -related metabolites, pyruvate was most strik- ingly increased in the case group. Interestingly, total choles- terol and triglyceride levels were not significantly different in the case group and changes in high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol were minor, but large and small HDL (L-HDL and S-HDL) particle numbers, total lipids, cholesterols and esterified cholesterols were significantly de- creased in the case group, and large LDL (L-LDL) variables were significantly increased except for triglycerides. Detection of overall disease occurrence using the meta- bolic profile The final multivariate logistic regression model included 6 metabolic measures: albumin, GlycA, pyruvate, alanine, glucose, and the ratio of phenylalanine to tyrosine. While the model was adjusted for age, age was not a significant predictor of disease in the model (Table 2). The average AUC of the 10-fold cross validated model was 0.82 (95% CI: 0.78 - 0.86), indicating excel- lent performance. Table 1. Importance of each variable included in the final multivariate model. Predictor Variable importance Albumin 100 GlycA 91.54 Pyruvate 84.99 Ala 59.01 Glucose 53.94 Phe / Tyr 39.86 age 0.00 Metabolite associations with specific diseases While some changes in metabolite concentrations were uni- versal across different diseases, multiple diseases also showed disease-specific changes (Figure 2). Figure 2 shows metabolite association with specific dis- ease states. negLog10p: absolute value of the log10 transformed logistic regression p-value adjusted for age, MAD from median: difference of the group median from the median of the control group as median absolute deviations of the control group. Abbreviations: AD: atopic dermatitis, AGE: acute gastroenteritis, KC: canine in- fectious respiratory disease, OA: osteoarthritis, OT: noncutaneous neoplasia, Paro: parodontitis, Pyo: pyometra, ST: cutaneous neo- plasia, UTI: urinary tract infection, FA: fatty acids, C: choles- terol, GlycA: glycoprotein acetyls, Ala: alanine, Gln: glutamine, Gly: glycine, His: histidine, BCAA: branched-chain amino acids, Ile: isoleucine, Leu: leucine, Val: valine, Phe: phenylalanine, Tyr: tyrosine, TG: triglycerides, VLDL: very low-density lipopro- tein, LDL: low-density lipoprotein, HDL: high-density lipoprotein, L: lipids, P: particles, PL: phospholipids, CE: esterified cho- lesterol, FC: free cholesterol, XL-: very large, L-: large, S-: small. The most universal changes across different diseases were the decrease of albumin and tyrosine and the increase in GlycA and pyruvate. An increase in phenylalanine and the phenylalanine / ty- rosine ratio was mainly only observed in the studied acute dis- eases. Changes in other amino acids were relatively disease-spe- cific, while all changes except phenylalanine were decreases in concentration. Multiple very low-density lipoprotein (VLDL) lipid variables and total triglycerides were increased in dogs with os- teoarthritis, parodontitis, neoplastic diseases and pyometra while being decreased in dogs with atopic dermatitis, acute gastroen- teritis, and canine infectious respiratory disease. HDL and LDL variables behaved differently. LDL variables, especially L-LDL variables, were increased in dogs with osteoarthritis, pyometra and noncutaneous neoplasia with no or little changes in other diseases. Total HDL and total cholesterol remained unaltered in all diseases, but small and large HDL variables were relatively uniformly decreased across multiple diseases including atopic der- matitis, acute gastroenteritis, canine infectious respiratory dis- ease, noncutaneous neoplasia and pyometra. In fatty acid concentrations, a decrease in Oleic acid, Docosapentaenoic acid and omega-3 fatty acids and an increase in saturated fatty acids was observed in pyometra. Decreased omega-3 polyunsaturated fatty acid (PUFA) concentrations were observed in parodontitis, pyom- etra, cutaneous neoplasia, and urinary tract infections. Citrate was decreased in atopic dermatitis, acute gastroenteritis and ca- nine infectious respiratory disease, and acetate roughly followed a similar pattern. Glucose was elevated in parodontitis and pyom- etra, slightly also in the other studied acute conditions. Gen- erally, dogs with atopic dermatitis had the fewest and slightest metabolic changes. GlycA and albumin detect chronic inflammatory conditions better than CRP and a combination of metabolic measures detects all studied diseases better than CRP The acute inflammatory marker CRP was not able to suffi- ciently detect any of the studied chronic inflammatory conditions atopic dermatitis, osteoarthritis, cutaneous neoplasia, noncuta- neous neoplasia and parodontitis, while the acute conditions acute gastroenteritis and urinary tract infection were acceptably de- tected, canine infectious respiratory disease excellently and pyometra outstandingly (Table 3). All chronic conditions were bet- ter detected by one or both of the chronic inflammatory markers GlycA and albumin alone than by CRP. All diseases were better detected by combinations of the metabolic measures than CRP. The final logistic regression model created for the full dataset best classified disease status in all other diseases than osteoarthri- tis and atopic dermatitis, where the combination of GlycA and albumin performed best. The performance of the final logistic re- gression model was acceptable in osteoarthritis, excellent in acute gastroenteritis, canine infectious respiratory disease, cu- taneous neoplasia, noncutaneous neoplasia, parodontitis and uri- nary tract infections and outstanding in pyometra (Figure 3). Figure 3 shows the ROC curves and AUC of the metabo- lite-based multivariate logistic regression model by disease. Ab- breviations: Pyo: pyometra, OT: noncutaneous neoplasia, AGE: acute gastroenteritis, KC: canine infectious respiratory disease, ST: cutaneous neoplasia, UTI: urinary tract infection, Paro: parodon- titis, OA: osteoarthritis, AD: atopic dermatitis. Table 2. Ability of CRP, GlycA, albumin, the combination of GlycA and albumin, and the final forward stepwise selected logistic regression model to correctly classify dogs as healthy or having one of the studied diseases. CRP AUC GlycA Albumin GlycA + Final (CI) AUC (CI) AUC (CI) Albumin AUC (CI) AUC (CI) AD 0.58 0.54 0.67 0.69 0.68 (0.53- (0.42- (0.56- (0.59- (0.58- 0.63) 0.66) 0.79) 0.80) 0.78) AGE 0.71 0.53 0.75 0.76 0.86 (0.63- (0.41- (0.64- (0.66- (0.79- 0.79) 0.64) 0.85) 0.87) 0.93) KC 0.84 0.69 0.62 0.69 0.85 (0.73- (0.56- (0.48- (0.55- (0.75- 0.94) 0.82) 0.76) 0.83) 0.95) OA 0.46 0.78 0.63 0.82 0.75 (0.40- (0.66- (0.51- (0.73- (0.63- 0.52) 0.90) 0.74) 0.92) 0.87) OT 0.58 0.73 0.70 0.86 0.89 (0.53- (0.63- (0.61- (0.79- (0.84- 0.64) 0.83) 0.80) 0.92) 0.94) Par 0.60 0.65 0.62 0.72 0.84 o (0.52- (0.55- (0.52- (0.62- (0.76- 0.67) 0.76) 0.72) 0.82) 0.91) Pyo 0.93 0.86 0.83 0.94 0.98 (0.84-1) (0.70-1) (0.72- (0.85-1) (0.94-1) 0.93) ST 0.54 0.61 0.68 0.78 0.82 (0.48- (0.47- (0.57- (0.69- (0.73- 0.61) 0.76) 0.78) 0.87) 0.91) UTI 0.70 0.69 0.64 0.73 0.82 (0.60- (0.55- (0.52- (0.59- (0.71- 0.80) 0.83) 0.77) 0.87) 0.93) The classification is based on logistic regression and the classification ability is expressed as area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Gen- erally, an AUC over 0.7 is considered acceptable, over 0.8 is considered excellent, and over 0.9 is considered outstanding. AD: atopic dermatitis, AGE: acute gastroenteritis, KC: canine infec- tious respiratory disease, OA: osteoarthritis, OT: noncutaneous neoplasia, Paro: parodontitis, Pyo: pyometra, ST: cutaneous neo- plasia, UTI: urinary tract infection. Conclusions Several clinically important findings were thus made: i) both chronic and acute inflammatory diseases cause metabolic changes; ii) it confirms a systemic inflammatory component in some diseases, such as osteoarthritis, that have been thought to cause only local inflammation; iii) GlycA and albumin better identified chronic inflammatory diseases than CRP; iv) the metabolite profile could better detect all studied inflammatory conditions than CRP. Both acute and chronic inflammatory diseases cause meta- bolic changes. These changes have the potential to be utilized in diagnostics of inflammatory conditions. Universal inflammatory changes can be clinically utilized to detect and evaluate an in- flammatory state. An increase in GlycA was observed in multiple inflammatory conditions. The GlycA signal combines the signals of several acute-phase proteins. Albumin is a negative inflammatory marker, decreasing in concentration during inflammatory diseases. Both of these markers better detected chronic inflammatory condi- tions than the acute inflammatory marker CRP. Combining GlycA and albumin increased the discriminatory capacity, and adding more metabolic measures to the model mostly further increased discrim- inatory power so that all diseases were better detected than using only CRP. This suggests, that laboratory diagnostics based on sin- gle markers might be outdated and that scores based on multiple markers could have the power to improve disease diagnostics. Osteoarthritis has previously been thought to cause only local inflammation. The present results confirm the presence of a low-grade systemic inflammatory response in dogs with osteoarthri- tis. These changes are not observable through diagnostic methods currently available on-clinic but might have implications on the management of these diseases. The metabolic changes observed in acute gastroenteritis included the increase in phenylalanine and the decrease in glycine, several fatty acids and lipoproteins, whereas similar changes were not observed in citrate and acetate. Dogs with atopic dermatitis displayed the fewest and slightest metabolic changes. CRP values could also not acceptably detect acute atopic dermatitis cases in this study. It is likely that a systemic association with disease severity is present in dogs and that systemic inflammatory markers could detect patients with moderate to severe disease that has a systemic component. Decreased omega-3 PUFA concentrations were observed in parodontitis, pyometra, cutaneous neoplasia, and urinary tract infections, Additionally, an increase in saturated fatty acids and decrease in the monounsaturated fatty acid Oleic acid was observed in pyometra. Dogs with acute conditions, especially pyometra, had higher glucose levels than healthy dogs. However, glucose was also higher in dogs with parodontitis. The increase of pyruvate in most inflammatory conditions suggests changes in cellular energy me- tabolism. The changes in citrate and lactate did not go hand in hand with this change, and showed only disease-specific changes. The changes in amino acids observed in the present results were decreases in concentration with the exception of phenylala- nine. These changes may arise from muscle catabolism and increased amino acid usage. In the present results, changes in VLDL and LDL variables were observed in diseases that are common in aging individuals such as with osteoarthritis, parodontitis, neoplastic diseases and pyometra. While the changes in VLDL and LDL might be truly inflam- mation-related, these groups might also have a higher number of overweight / obese individuals, which might contribute to the in- creased concentration of these lipoproteins. Small and large HDL variables were the lipoprotein variables most uniformly associated with inflammatory diseases in the present results, whereas very large HDL particles remained unaltered. There were differences in sex and sterilization status between the individual disease groups and the control group that could not be taken into account in statistical analyses. However, the studied metabolites are only minimally affected by sex and sterilization status. Additionally, all groups consisted of dogs of a large variety of different breeds and while breed affects metabolite concentrations, no single breed or breed type dominated any of the groups. The findings of this study cannot be explained by only group differences in these demographical factors. It must be noted, that since CRP was used as an exclusion criteria for the healthy control group, the discriminatory capacity of CRP might be worse in a typical population of clinically healthy dogs. Thus the present results describe both the individual and common metabolic changes caused by these conditions. The metabo- lite profile could excellently detect all studied conditions, and had a better discriminatory capacity than CRP. These results pave the way for a much-needed testing method for low-grade chronic inflammation in veterinary medicine. It is obvious to a person skilled in the art that with the advancement of technology, the basic idea may be implemented in various ways. The embodiments are thus not limited to the examples described above; instead they may vary within the scope of the claims. The embodiments described hereinbefore may be used in any combination with each other. Several of the embodiments may be combined together to form a further embodiment. A method, a product, or a use, disclosed herein, may comprise at least one of the embodiments described hereinbefore. It will be understood that the benefits and advantages described above may relate to one embodiment or may relate to several embodiments. The embodiments are not limited to those that solve any or all of the stated problems or those that have any or all of the stated benefits and advantages. It will further be understood that reference to 'an' item refers to one or more of those items. The term “comprising” is used in this specification to mean including the feature(s) or act(s) followed thereafter, without excluding the presence of one or more additional features or acts.

Claims

CLAIMS 1. A method for detecting or assessing an inflammatory state or condition in a canine subject, and / or determining whether the canine subject is at risk of developing the inflammatory state or condition, wherein the method comprises determining in a biological sample obtained from the canine subject quantitative values of the following biomarkers: - glycoprotein acetyls, and - albumin; and optionally a quantitative value of at least one biomarker of the following: - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition.

2. The method according to claim 1, wherein the method comprises determining in the biological sample quantitative values of at least two, or at least three biomarkers of the following: - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, or - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition.

3. The method according to claim 1 or 2, wherein the method comprises determining in the biological sample quantitative values of the following biomarkers: - glycoprotein acetyls, - albumin, - pyruvate, - a ratio of phenylalanine to tyrosine and / or tyrosine, - alanine, and - glucose; and comparing the quantitative values of the biomarkers to a control sample or to a control value; wherein an increase or a decrease in the quantitative values of the biomarkers, when compared to the control sample or to the control value, is / are indicative of the inflammatory state or condition in the canine subject and / or of the canine subject having an increased risk of developing the inflammatory state or condition.

4. The method according to any one of claims 1 – 3, wherein the inflammatory condition comprises or is at least one of pyometra, acute gastroenteritis, urinary tract infection, or canine infectious respiratory disease.

5. The method according to any one of claims 1 – 4, wherein the inflammatory condition comprises or is at least one of atopic dermatitis, parodontitis, osteoarthritis, or a neoplastic disease, such as cutaneous neoplasia or noncutaneous neoplasia.

6. The method according to any one of claims 1 – 5, wherein the method further comprises determining in the biological sample quantitative values of at least one biomarker of the following: - at least one of cholesterol in very low density lipoprotein (VLDL), triglycerides in VLDL, or total lipids in VLDL, - total triglycerides, - at least one of cholesterol in low density lipoprotein (LDL), triglycerides in LDL, total lipids in LDL, cholesterol in large low density lipoprotein (L-LDL), triglycerides in L-LDL, or total lipids in L-LDL, - at least one of small high density lipoprotein (S-HDL) particles, total lipids in S-HDL, phospholipids in S-HDL, cholesterol in S-HDL, esterified cholesterol in S-HDL, or free cholesterol in S-HDL, - at least one of large high density lipoprotein (L-HDL) particles, total lipids in L-HDL, phospholipids in L- HDL, cholesterol in L-HDL, esterified cholesterol in L-HDL, and free cholesterol in L-HDL, - oleic acid, - docosapentaenoic acid, - omega-3 fatty acids, - saturated fatty acids, - polyunsaturated fatty acids (PUFAs), - citrate, - acetate, or - lactate.

7. The method according to any one of claims 1 – 6, wherein the method further comprises determining in the biological sample quantitative value(s) of - at least one of cholesterol in very low density lipoprotein (VLDL), triglycerides in VLDL, or total lipids in VLDL, and / or - total triglycerides.

8. The method according to any one of claims 1 – 7, wherein the method further comprises determining in the biological sample quantitative value(s) of at least one of cholesterol in low density lipoprotein (LDL), triglycerides in LDL, total lipids in LDL, cholesterol in large low density lipoprotein (L-LDL), triglycerides in L-LDL, or total lipids in L-LDL.

9. The method according to any one of claims 1 – 8, wherein the method further comprises determining in the biological sample quantitative value(s) of - at least one of small high density lipoprotein (S-HDL) particles, total lipids in S-HDL, phospholipids in S- HDL, cholesterol in S-HDL, esterified cholesterol in S-HDL, or free cholesterol in S-HDL, and / or - at least one of large high density lipoprotein (L-HDL) particles, total lipids in L-HDL, phospholipids in L- HDL, cholesterol in L-HDL, esterified cholesterol in L-HDL, and free cholesterol in L-HDL.

10. The method according to any one of claims 1 – 9, wherein the method further comprises determining in the biological sample quantitative value(s) of - oleic acid, - docosapentaenoic acid, - omega-3 fatty acids, and / or - saturated fatty acids.

11. The method according to any one of claims 1 – 10, wherein the method further comprises determining in the biological sample quantitative value of polyunsaturated fatty acids (PUFAs).

12. The method according to any one of claims 1 – 11, wherein the method further comprises determining in the biological sample quantitative value(s) of - citrate, and / or - acetate.

13. The method according to any one of claims 1 - 12, wherein the method further comprises detecting or assessing the inflammatory state or condition in the canine subject and / or determining whether the canine subject is at risk of developing the inflammatory state or condition using a diagnostic score, likelihood, risk score, hazard ratio, odds ratio, and / or predicted absolute risk or relative risk calculated on the basis of at least the quantitative values of the biomarkers.

14. The method according to any one of claims 1 – 13, wherein the quantitative values of the biomarkers are measured using nuclear magnetic spectroscopy.

15. The method according to any one of claims 1 – 14, wherein the biological sample is a blood sample, a plasma sample, a serum sample, or a sample derived therefrom.

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