ES2734810T3 - Use of SM_Sphingomyelin (D18: 1, C16: 0) as a marker for heart failure - Google Patents

Abstract

A procedure for diagnosing heart failure in a subject, comprising the steps of: a) determining, in a sample of a subject suspected of having heart failure, the amount of at least one biomarker, being at least one biomarker (i ) SM_Sphingomyelin (d18: 1, C16: 0) or (ii) SM_Sphingomyelin (d18: 1, C16: 0) and at least one biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8A2, 8A2, 8A2, 8A2, 8A2 8B2; b) compare the amount of said at least one biomarker with a reference, whereby heart failure is to be diagnosed.

Description

DESCRIPTION

Use of SM_Sphingomyelin (D18: 1, C16: 0) as a marker for heart failure

The present invention relates to the field of diagnostic procedures. Specifically, the present invention contemplates a method for diagnosing heart failure in a subject and a procedure for monitoring the progression or regression of heart failure in a subject. The invention also relates to tools for carrying out the aforementioned procedures, such as diagnostic devices.

Heart failure is a serious problem in modern medicine. Impaired heart function may increase ailments that pose a threat to life and result in discomfort to patients suffering from heart failure. Heart failure can affect the right side or the left side of the heart, respectively, and may vary in intensity. The New York Heart Association (NYHA) originally developed a classification system. According to the classification system, mild cases of heart failure are classified as class I cases. These patients only show symptoms under extreme exercise. The intermediate cases show more pronounced symptoms with less exercise (classes II and III) while class IV already shows symptoms at rest (New York Heart Association. Diseases of the heart and blood vessels. Nomenclature and criteria for diagnosis, 6th ed Boston: Little, Brown et al., 1964; 114).

The prevalence of heart failure steadily increases in the population of developed western countries in recent years. A reason for this increase can be seen in an increased average life expectancy due to modern medicine. The mortality rate caused by heart failure, however, could be further reduced by improved diagnosis and therapeutic solutions. The so-called "Framingham" study reported a 5-year reduction in mortality from 70% to 59% in men and from 57% to 45% in women when comparing the time window from 1950 to 1969 with 1990 to 1999. The "Mayo" study shows a 65% to 50% reduction in men for the time window from 1996 to 2000 compared to 1979 to 1984 and from 51% to 46% for women. Despite this reduction in the mortality rate, overall mortality due to heart failure is an important burden for societies. One-year mortality for NY-HA class II to III patients with ACE inhibitor treatment remains between 9-12% (RESOLVED) and for NYHA class IV without ACE inhibitor treatment, 52% (Consensus).

Diagnostic techniques such as echocardiography depend on the experience of the individual investigator and, therefore, are not always reliable. In addition, these techniques sometimes fail to diagnose the early onset of heart failure. Biochemical tests that are based on cardiac hormones such as brain natriuretic peptides (BNP) are also altered by other diseases and disorders such as kidney failure or depend on the patient's overall physical ailment. However, brain natriuretic peptides are the current gold standard for biochemically evaluating heart failure. According to a recent study comparing BNP and pro-BNP N-terminal (NT-proBNP) in the diagnosis of heart failure, BNP is a better indicator of heart failure and left ventricular systolic dysfunction than NT-proBNP. In symptomatic patient groups, a diagnostic probability index of 27 for ^b N ^p compares a sensitivity of 85% and a specificity of 84% in the detection of heart failure (Ewald 2008, Intern Med J 38 (2): 101- 13.).

Ueland et al. (2006), J Cardiac Failure 12 (8): 659 documented circulating levels of soluble Toll-type receptor 2 and mannose-binding lectin after acute myocardial infarction. Chatterjee et al. (2008) Curr Op Inv Drugs 7 (3): 219 reviewed the role of the sphingomyelin phospholipid in heart disease, while Chen et al. (2011), Nutrition & Metabolism 8:25 documented the impact of sphingomyelin levels on coronary heart disease and left ventricular systolic function in humans. In addition, Yetukuri et al. (2010), J Lipid Res 51: 2341 analyzed the composition and spatial distribution of lipid of HDL particles in subjects with low and high cholesterol1HDL. However, it is a goal of modern medicine to identify with reliability and treat patients with heart failure and, in particular, to identify them at the early onset of heart failure, that is, in the initial stages I to III of the NYHA and in particular, in stage I of the NYHA. Accordingly, the means to reliably diagnose heart failure are highly desired but not yet available.

The present invention relates to a method for diagnosing heart failure in a subject according to claim 1.

The present disclosure relates to a procedure for diagnosing heart failure in a subject, comprising the steps of:

a) determine, in a sample of a subject suspected of having heart failure, the amount of at least one biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1,2A2, 2B1,2B2, 3A1 , 3A2, 3B1, 3B2, 4A1, 4A2, 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1 or 8B2;

b) compare the quantity of the mentioned at least one biomarker with a reference, whereby it is going to diagnose heart failure

The method cited in accordance with the present invention includes a procedure consisting essentially of the above-mentioned steps or a procedure that includes additional steps. However, it should be understood that the procedure is a procedure carried out ex vivo, that is, not practiced in the human or animal body. The process, preferably, can be assisted by automation.

The term "diagnose", as used herein refers to assessing whether a subject suffers from heart failure, or not. As those skilled in the art will understand, such evaluation, although it is preferred that it be corrected, may not normally be corrected for 100% of the subjects investigated. The term, however, requires that a statistically significant part of subjects can be correctly assessed and therefore diagnosed. The person skilled in the art can determine if a portion is statistically significant without further delay, using various known statistical evaluation tools, for example, the determination of confidence intervals, determination of the p-value, student's t-test, Mann's test -Whitney, etc. Details are found in Dowdy and Wearden, for Research, John Wiley & Sons, New York 1983. Preferred confidence intervals are at least 50%, at least 60%, at least 70%, at least 80% , at least 90% or at least 95%. P values are preferably 0.2, 0.1, or 0.05.

The term includes the individual diagnosis of heart failure or its symptoms as well as the continuous monitoring of a patient. Surveillance, that is, the diagnosis of the presence or absence of heart failure or the symptoms that accompany it at various time points, includes the surveillance of patients known to have heart failure as well as the surveillance of subjects known to be They are at risk of developing heart failure. In addition, surveillance can also be used to determine if a patient has been treated satisfactorily or if at least the symptoms of heart failure can be improved over time by a certain treatment. In addition, the term also includes classifying a subject according to the classes of the New York Heart Association (NYHA) for heart failure. According to this classification, heart failure can be subdivided into four classes. Subjects presenting class I do not show limitation in activities except under strong physical exercise. Subjects presenting class II show a limitation of light, mild, although comfortable at rest or mild exercise activity. The subjects presenting class III show a marked limitation of any activity, although comfortable only at rest. Subjects presenting class IV show discomfort and symptoms even at rest. Preferably, the heart failure to be determined in accordance with the present invention is asymptomatic heart failure, that is, heart failure according to NYHA class I, or symptomatic heart failure, i.e. heart failure. at least according to NYHA class II and / or III.

In one disclosure, said subjects suffer from asymptomatic heart failure and the at least one biomarker is a biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1 , 3B2, 4A1, 4A2, 4B1 or 4B2. More preferably, said asymptomatic heart failure in the subject is heart failure according to NYHA class I.

In another disclosure, said subjects suffer from symptomatic heart failure and the at least one biomarker is a biomarker selected from the related biomarkers in Table 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1 or 8B2. More preferably, said symptomatic heart failure in the subject is heart failure according to NYHA class II and / or III.

The American Heart Association provides another classification system. Four stages of heart failure are subdivided: Stage A: Patients with a high risk of developing HF in the future but without functional or structural heart disorder. Stage B: a structural heart disorder but without symptoms at any stage. Stage C: previous or current symptoms of heart failure in the context of an underlying structural heart problem, but managed with medical treatment. Stage D: advanced disease that requires hospital support, a heart transplant or palliative care. It will be understood that the method of the present invention can also be used to classify heart failure according to this system, preferably, the biomarkers identified should diagnose heart failure according to stages A to C and to discriminate between stages A and B asymptomatic and stage C, more severe, that is, symptomatic heart failure.

The term "heart failure" as used herein refers to impaired function of the heart. The aforementioned deterioration may be a systolic dysfunction that results in a significantly reduced ejection fraction of the blood coming from the heart and, therefore, a reduced blood flow. Specifically, systolic heart failure is characterized by a significantly reduced left ventricular ejection fraction (LEVF), preferably, an ejection fraction of less than 55%. Alternatively, the deterioration may be a diastolic dysfunction, that is, a ventricle failure to relax properly. The latter is normally accompanied by a more rigid ventricular wall. Diastolic dysfunction causes an inadequate ventricle load and, therefore, results in blood flow, in general. Therefore, diastolic dysfunction also results in high diastolic pressures, and the end result is comparable to the case of systolic dysfunction (pulmonary edema in heart failure on the left side, peripheral edema in heart failure on the right side). Heart failure can affect, therefore, the right side of the heart (pulmonary circulation), the left side of the heart (body circulation) or both. Techniques for measuring impaired heart function and, therefore, heart failure, are well known in the art and include echocardiography, electrophysiology, angiography, and the determination of peptide biomarkers, such as cerebral natriuretic peptide (BNP). or the N-terminal fragment of its propeptide, in the blood. It will be understood that impaired heart function can occur permanently or only under conditions of a particular stress or exercise. Depending on the intensity of the symptoms, heart failure can be classified as shown elsewhere in this document. Typical symptoms of heart failure include dyspnea, chest pain, dizziness, confusion, pulmonary and / or peripheral edema. It will be understood that the incidence of symptoms as well as their severity may depend on the severity of heart failure and the characteristics and causes of heart failure, systolic or diastolic or restrictive, that is, heart failure located on the right or left side. from the heart. Symptoms of heart failure are well known in the art and are described in the usual medical textbooks, such as Stedman or Brunnwald.

Preferably, heart failure, as used herein, refers to dilatory cardiomyopathy (DCMP), ischemic cardiomyopathy (ICMP) or hypertrophic cardiomyopathy (HCMP).

In one disclosure, said asymptomatic heart failure is DCMP and said at least one biomarker is a biomarker is selected from the related biomarkers in Table 2A1, 2A2, 2B1 or 2B2. In a further disclosure, said asymptomatic heart failure is ICMP and said at least one biomarker is a biomarker is selected from the related biomarkers in Table 3A1, 3A2, 3B1 or 3B2. Preferably, said asymptomatic heart failure is HCMP and said at least one biomarker is a biomarker is selected from the related biomarkers in Table 4A1, 4A2, 4B1 or 4B2.

In a further disclosure, said symptomatic heart failure is DCMP and said at least one biomarker is selected from the related biomarkers in Table 6A1, 6A2, 6B1 or 6B2. Preferably, said symptomatic heart failure is ICMP and said at least one biomarker is selected from the related biomarkers in Table 7A1, 7A2, 7B1 or 7B2. In a further disclosure, said symptomatic heart failure is HCMP and said the at least one biomarker is a biomarker is selected from the related biomarkers in Table 8A1, 8A2, 8B1 or 8B2.

The term "biomarker" as used herein refers to a molecular species that serves as an indicator of a disease or influences as referred to herein. Said molecular species can be a metabolite by itself found in a sample of a subject. In addition, the biomarker can also be a molecular species that is derived from said metabolite. In that case, the actual metabolite will be chemically modified in the sample or during the determination procedure and, as a result of such modification, a chemically different molecular species will be determined, that is, the analyte. It should be understood that in that case, the analyte represents the actual metabolite and has the same potential as an indicator for the respective medical condition.

In the process according to the present invention, at least one metabolite of the aforementioned group of biomarkers is to be determined. However, more preferably, a group of biomarkers will be determined to reinforce the specificity and / or sensitivity of the evaluation. Said group preferably comprises at least 2, at least 3, at least 4, at least 5, at least 10 or even all of the aforementioned biomarkers shown in the respective Tables. In addition to the specific biomarkers listed in the specification, other biomarkers can preferably also be determined in the methods of the present invention.

A metabolite, as used herein, refers to at least one molecule of a specific metabolite up to a plurality of molecules of said specific metabolite. It should also be understood that a group of metabolites means a plurality of chemically different molecules in which at least one molecule can be present for each metabolite to a plurality of molecules. A metabolite according to the present invention encompasses all kinds of organic or inorganic chemical compounds including those that are comprised of biological material such as organisms. Preferably, the metabolite according to the present invention is a small molecule compound. More preferably, in the case that a plurality of metabolites is encompassed, said plurality of metabolites represents a metabolome, that is, the set of metabolites comprising an organism, an organ, a tissue, a body fluid or a cell at a time. specific and under specific conditions.

Metabolites are small molecule compounds, such as enzyme substrates of metabolic pathways, intermediates of said pathways or products obtained by a metabolic pathway. Metabolic pathways are well known in the art and may vary between species. Preferably, said routes include at least the citric acid cycle, the respiratory chain, glycolysis, glyconeogenesis, the hexose monophosphate route, the oxidative route of the phosphate pentoses, the production and the p-oxidation of fatty acids, the urea cycle, amino acid biosynthesis pathways, protein degradation pathways such as proteosomal degradation, amino acid degradation pathways, biosynthesis or degradation of: lipids, polyketides (including, for example , flavonoids and isoflavonoids), isoprenoids (including, for example, terpenes, sterols, steroids, carotenoids, xanthophylls), carbohydrates, phenylpropanoids and their derivatives, alkaloids, benzenoids, idols, indole-sulfur compounds, porphyrins, anthocyanins, hormones, vitamins, cofactors such as prosthetic groups or electron transporters, lignin, glucosinolates, purines, pyrimidines, nucleosides, nucleotides and related molecules such as tRNA, microRNA (miRNA) or mRNA. Accordingly, the metabolites of small molecule compounds are preferably composed of the following classes of compounds; alcohols, alkanes, alkenes, alkynes, aromatic compounds, ketones, aldehydes, carboxylic acids, esters, amines, imines, amides, cyanides, amino acids, peptides, thiols, thioesters, phosphate esters, sulfate esters, thioethers, sulfoxides, ethers, or combinations or derivatives of the aforementioned compounds. Small molecules between metabolites can be primary metabolites that are required for normal cell function, organic function or animal growth, development or health. In addition, small molecule metabolites further comprise secondary metabolites that have an essential ecological function, for example, metabolites that allow an organism to adapt to its environment. In addition, metabolites are not limited to said primary and secondary metabolites and also encompass artificial compounds of small molecules. Such artificial compounds of small molecules are derived from small molecules provided exogenously that are administered or captured by an organism, but which are not primary or secondary metabolites as defined above. For example, artificial small molecule compounds can be metabolic products obtained from drugs by the animal's metabolic pathways. In addition, metabolites also include peptides, oligopeptides, polypeptides, oligonucleotides and polynucleotides, such as RNA or DNA. More preferably, a metabolite has a molecular weight of 50 Da (Dalton) to 30,000 Da, more preferably less than 30,000 Da, less than 20,000 Da, less than 15,000 Da, less than 10,000 Da, less than 8,000 Da, less than 7,000 Da , less than 6,000 Da, less than 5,000 Da, less than 4,000 Da, less than 3,000 Da, less than 2,000 Da, less than 1,000 Da, less than 500 Da, less than 300 Da, less than 200 Da, less than 100 Da . Preferably, a metabolite has, however, a molecular weight of at least 50 Da. Most preferably, a metabolite according to the present invention has a molecular weight of 50 Da to 1,500 Da.

The term "sample" as used herein refers to samples of body fluids, preferably blood, plasma, serum, saliva or urine, or samples derived, for example, by biopsy, from cells, tissues or organs, particularly from the heart. More preferably, the sample is a blood, plasma or serum sample, most preferably, a plasma sample. In the case of said blood, plasma or serum sample, preferably, the at least one biomarker to be determined in accordance with the method of the present invention is a biomarker as related in any one of Tables 1A1, 1A2, 2A1, 2A2, 3A1, 3A2, 4A1, 4A2, 5A1, 5A2, 6A1, 6A2, 7A1, 7A2, 8A1 or 8A2. More preferably, the sample is a urine sample. In the case of said urine sample, preferably, the at least one biomarker to be determined according to the method of the present invention is a biomarker as related in any one of Tables 1B1, 1 B2, 2B1, 2B2 , 3B1, 3B2, 4B1, 4B2, 5B1, 5B2, 6B1, 6B2, 7B1, 7B2, 8B1 or 8B2. Biological samples may be derived from a subject as specified elsewhere in this document. The techniques for obtaining the different types of biological samples mentioned above are well known in the art. For example, blood samples can be obtained by drawing blood while obtaining samples of tissues or organs, for example, by biopsy.

The aforementioned samples are preferably pretreated before being used for the process of the present invention. As described in more detail below, said pretreatment may include the treatments required to release or separate the compounds or to remove excess material or waste. Suitable techniques include centrifugation, extraction, fractionation, ultrafiltration, protein precipitation followed by filtration and purification and / or enrichment of compounds. In addition, other pretreatments are carried out in order to provide the compounds in a form or concentration suitable for the analysis of the compounds. For example, if mass spectrometry coupled to gas chromatography is used in the process of the present invention, it will be necessary to derivatize the compounds prior to said gas chromatography. Adequate and necessary pretreatments depend on the means used to carry out the process of the invention and are well known to those skilled in the art. Pretreated samples as described above are also encompassed by the term "sample" as used in accordance with the present invention.

The term "subject" as used herein refers to animals and, preferably, mammals. More preferably, the subject is a primate and, most preferably, a human being. The subject, preferably, is suspected of having heart failure, more preferably, may already show some or all of the symptoms associated with the disease. However, also included as subjects suspected of suffering from heart failure are those, who belong to the risk groups or subjects that are included in the disease screening projects or measures. More preferably, the subject is an asymptomatic subject presenting symptoms according to NYHA classes I or a symptomatic subject presenting symptoms according to NYHA class II and / or III. In addition, the subject must also have congestive systolic heart failure due to contractile dysfunction such as dilated cardiomyopathy. Preferably, the subject, however, despite the above-mentioned diseases and disorders is apparently healthy. In particular, it should preferably not present symptoms according to NYHA class IV patients or suffer from stroke, myocardial infarction in at least the last 4 months after the sample has been taken or acute or chronic inflammatory diseases and tumors. malignant In addition, the subject is preferably stably medicated for at least 4 weeks before the sample has been taken.

The term "determine the amount" as used herein refers to determining at least one characteristic feature of a biomarker to be determined by the method of the present invention in the sample. The characteristic features according to the present invention are features that characterize the physical and / or chemical properties including the biochemical properties of a biomarker. Such properties include, for example, molecular weight, viscosity, density, electric charge, spin, optical activity, color, fluorescence, chemiluminescence, elemental composition, chemical structure, the ability to react with other compounds, the ability to stimulate a response in a biological reading system (for example, induction of an indicator gene) and the like. The values of said properties can serve as characteristic features and can be determined by techniques well known in the art. In addition, the characteristic feature can be any feature that derives from the values of the physical and / or chemical properties of a biomarker through standardized operations, for example, mathematical calculations such as multiplication, division or logarithmic calculation. Most preferably, the at least one characteristic feature allows the determination and / or chemical identification of said at least one biomarker and its amount. Therefore, the characteristic value preferably also includes information that refers to the abundance of the biomarker from which the characteristic value is derived. For example, a characteristic value of a biomarker can be a peak in a mass spectrum. Said peak contains characteristic information of the biomarker, that is, the m / z information, as well as an intensity value that is related to the abundance of said biomarker (ie, its quantity) in the sample.

As described above, each biomarker comprised of a sample can preferably be determined in accordance with the present invention in a quantitative or semi-quantitative manner. For the quantitative determination, the absolute or precise amount of the biomarker will be determined or the relative amount of the biomarker will be determined based on the value determined for each characteristic feature (s) referred to herein above. The relative amount can be determined in a case where the precise amount of a biomarker cannot or should not be determined. In that case, it can be determined whether the amount in which the biomarker is present increases or decreases with respect to a second sample comprising said biomarker in a second quantity. In a preferred embodiment, said second sample comprising said biomarker should be a reference calculated as specified elsewhere herein. Analyzing a biomarker quantitatively, therefore, also includes what is sometimes referred to as a semiquantitative analysis of a biomarker.

In addition, determining how it is used in the process of the present invention preferably includes using a step of separating the compound before the analysis step cited above. Preferably, said compound separation step results in a resolved separation time of the metabolites comprised by the sample. Suitable separation techniques that are preferably to be used in accordance with the present invention, therefore, include all chromatographic separation techniques such as liquid chromatography (LC), high performance liquid chromatography (HPLC), gas chromatography. (GC), thin layer chromatography, size exclusion chromatography or affinity chromatography. These techniques are well known in the art and can be applied by the person skilled in the art without further ado. Most preferably, LC and / or GC are chromatographic techniques to be contemplated in the process of the present invention. Suitable devices for said biomarker determination are well known in the art. Preferably, mass spectrometry is used, in particular mass spectroscopy gas chromatography (GC_MS), mass spectrometry liquid chromatography (LC-MS), mass spectrometry direct infusion or Fourier transformation - mass spectrometry by cyclotron resonance (FT-ICR-MS), mass spectrometry using capillary electrophoresis (CE-MS), mass spectrometry coupled to high performance liquid chromatography (HPLC-MS), quadrupole mass spectrometry, any sequentially coupled mass spectrometry, such such as MS-MS or MS-MS-MS, inductively coupled plasma mass spectrometry (ICP-MS), pyrolysis mass spectrometry (Py-MS), ion mobility mass spectrometry or flight time mass spectrometry (TOF). Most preferably, LC-MS and / or GC-MS are used as described in detail below. Such techniques are disclosed in, for example, Nissen 1995, Journal of Chromatography A, 703: 37-57, US 4,540,884 or US 5,397,894. As an alternative or in addition to mass spectrometry techniques, the following techniques can be used for the determination of the compound: nuclear magnetic resonance imaging (NMR), Magnetic resonance imaging (MRI), Fourier transform infrared analysis ( FT-IR), ultraviolet (UV) spectroscopy, refractive index (IR), fluorescent detection, radiochemical detection, electrochemical detection, light scattering (LS), dispersive Raman spectroscopy or flame ionization detection (FID). These techniques are well known to the person skilled in the art and can be applied without further ado. The process of the present invention should preferably be assisted by automation. For example, sample processing or pretreatment can be automated by robotic means. The data processing and comparison, preferably, is assisted by suitable computerized programs and databases. Automation, as described hereinbefore, allows the method of the present invention to be used in high performance solutions.

In addition, the at least one biomarker can also be determined by a specific chemical or biological test. Said test shall comprise means that allow specifically detecting the at least one biomarker in the sample. Preferably, said means are capable of specifically recognizing the chemical structure of the biomarker or are capable of specifically identifying the biomarker based on its ability to react with other compounds or its ability to stimulate a response in a biological reading system (e.g. induction of an indicator gene). Means that are capable of specifically recognizing the chemical structure of a biomarker are preferably antibodies or other proteins that specifically interact with chemical structures, such as receptors or enzymes. For example, specific antibodies can be obtained using the biomarker as an antigen by methods well known in the art. Antibodies, as referenced herein, include polyclonal and monoclonal antibodies, as well as their fragments, such as Fv, Fab and F (ab) 2 fragments that are capable of binding to the antigen or hapten. The present invention also includes humanized hybrid antibodies in which the amino acid sequences of a non-human donor antibody having a specificity for the desired antigen are combined with sequences of a human acceptor antibody. In addition, single chain antibodies are encompassed. Donor sequences will usually include at least the donor antigen-binding amino acid residues but may also comprise other structurally and / or functionally relevant amino acid residues of the donor antibody. Such hybrids can be prepared by various procedures well known in the art. Suitable proteins that are capable of specifically recognizing the biomarker are preferably enzymes that are involved in the metabolic conversion of said biomarker. Such enzymes can use both the biomarker and a substrate or they can convert a substrate into the biomarker. In addition, said antibodies can be used as the basis for generating oligopeptides that specifically recognize the biomarker. These oligopeptides must comprise, for example, the enzyme binding domains or the pockets for said biomarker. Suitable antibody and / or enzyme based assays may be RIA (radioimmunoassay), ELISA (adsorption enzyme immunoassay), sandwich immunoenzymatic assays, electrochemiluminescence sandwich immunoassays (ECLIA), lanthanide fluoroimmunoassays or DELIA enhanced) solid phase immunoassays. In addition, the biomarker can also be determined based on its ability to react with other compounds, that is, by a specific chemical reaction. In addition, the biomarker in a sample can be determined due to its ability to stimulate a response in a biological reading system. The biological response should be detected as a reading indicating the presence and / or quantity of the biomarker included in the sample. The biological response can be, for example, the induction of gene expression or a phenotypic response of a cell or organism. In a preferred embodiment, the determination of the at least one biomarker is a quantitative procedure, for example, which also allows the determination of the amount of the at least one biomarker in the sample.

As described above, said determination of at least one biomarker may preferably comprise mass spectrometry (MS). Mass spectrometry as used herein encompasses all techniques that allow the determination of molecular weight (i.e., mass) or a variable mass that corresponds to a compound, that is, a biomarker, to be determined. in accordance with the present invention. Preferably, mass spectrometry as used herein refers to GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS, CE-MS, HPLC-MS, quadrupole mass spectrometry , any sequentially coupled mass spectrometry such as ^{m S-MS} or MS-MS-MS, ICP-MS, Py-MS, TOF or any combined solutions using the aforementioned techniques. How to apply these techniques is well known to the person skilled in the art. In addition, suitable devices are commercially available. More preferably, mass spectrometry as used herein refers to LC-MS and / or GC-MS, that is, mass spectrometry that is operatively linked to an earlier chromatographic separation stage. More preferably, mass spectrometry as used herein encompasses quadrupole MS. Most preferably, the quadrupole MS is carried out as follows: a) selection of a mass / charge ratio (m / z) of an ion created by ionization in a first analytical quadrupole of the mass spectrometer, b) fragmentation of the ion selected in step a) by applying an acceleration voltage on an additional rear quadrupole that is charged with a collision gas and acts as a collision chamber, c) selection of a mass / charge ratio of an ion created by the fragmentation process in stage b) in an additional posterior quadrupole, whereby steps a) to c) of the procedure are carried out at least once and the mass / charge ratio analysis of all the ions present in the mixing of substances as a result of the ionization process, whereby the quadrupole is charged with the collision gas but the acceleration voltage is not applied during the analysis. Details of said most preferred mass spectrometry to be used in accordance with the present invention can be found in WO 03/073464.

More preferably, said mass spectrometry is MS liquid chromatography (LC) and / or MS gas chromatography (GC). Liquid chromatography as used herein refers to all techniques that allow the separation of compounds (i.e. metabolites) in the liquid or supercritical phase. Liquid chromatography is characterized in that the compounds in a mobile phase are passed through the stationary phase. When the compounds pass through the stationary phase at different speeds they become separated in time because each individual compound has its specific retention time (i.e., the time required for the compound to pass through the system). Liquid chromatography as used herein also includes HPLC. Devices for liquid chromatography are commercially available, for example, from Agilent Technologies, USA. Gas chromatography as applied according to the present invention, in principle, works in a manner comparable to liquid chromatography. However, instead of having the compounds (that is, metabolites) in a liquid mobile phase that passes through the stationary phase, the compounds will be present in a gaseous volume. The compounds pass through the column that can contain solid support materials as a stationary phase or whose walls can serve as or are coated with the stationary phase. Likewise, each compound has a specific time that is necessary to pass through the column. Furthermore, in the case of gas chromatography, it is preferably contemplated that the compounds be derivatized before gas chromatography. Suitable techniques for derivatization are well known in the art. Preferably, derivatization according to the present invention refers to the methoximation and trimethylsilylation of, preferably, polar compounds and the transmethylation, methoximation and trimethylsilylation of, preferably, non-polar compounds (i.e., lipophilic).

The term "reference" refers to the characteristic trait values of each of the biomarkers that may be correlated with a medical condition, that is, the presence or absence of the disease, pathologies or an effect referred to herein. Preferably, a reference is a threshold value (for example, a quantity or quantity ratio) of a biomarker, whereby, the values found in a sample to be investigated that are greater or essentially identical to the threshold are indicative. of the presence of a medical condition while those that are minor are indicative of the absence of the medical condition. It will be understood that also preferably, a reference may be a threshold value of a biomarker whereby the values found in a sample to be investigated that are less than or identical to the threshold are indicative of the presence of a medical condition while Those who are older are indicative of the absence of a medical condition.

According to the above-mentioned method of the present invention, a reference is preferably a reference obtained from a sample of a subject or from a group of subjects known to have heart failure. In that case, a value for the at least one biomarker found in the test sample that is essentially identical is indicative of the presence of the disease. In addition, the reference, also preferably, could be from a subject or from a group of subjects known to have heart failure, preferably a seemingly healthy subject. In that case, a value for the at least one biomarker that is in the test sample that is altered with respect to the reference is indicative of the presence of the disease. The same applies analogously by making the necessary changes for a calculated reference, most preferably, the average or the median, for the relative or absolute value of the at least one biomarker of a population of individuals comprising the subject to be research. The absolute or relative values of the at least one biomarker of said individuals in the population can be determined as specified elsewhere in this document. How to calculate an appropriate reference value, preferably the average or the median, is well known in the art. The population of subjects referred to above should comprise a plurality of subjects, preferably, at least 5, 10, 50, 100, 1,000 or 10,000 subjects. It should be understood that the subject to be diagnosed by the method of the present invention and the subjects of said plurality of subjects are of the same species.

The value of the at least one biomarker of the test sample and the reference values are essentially identical, if the values for the characteristic traits and, in the case of quantitative determination, the intensity values are essentially identical. Essentially identical means that the difference between two values is preferably not significant and should be characterized in that the values for intensity are in at least the interval between the 1st and 99th percentile, the 5th and the 95th percentile, the 10th and 90th percentile, the 20th and 80th percentile, the 30th and 70th percentile, the 40th and 60th percentile of the reference value, preferably the 50th, 60th, 70 °, 80 °, 90 ° or 95th percentile of the reference value. The statistical test to determine whether two quantities are essentially identical is well known in the art and is also described elsewhere herein.

An observed difference for two values, on the other hand, must be statistically significant. A difference in relative or absolute value is preferably significantly outside the range between the 45th and 55th percentile, the 40th and 60th percentile, the 30th and 70th percentile, the 20th and 80th percentile, 10th and 90th percentile, 5th and 95th percentile, 1st and 99th percentile of the reference value. Preferred changes and relationships of the medians are described in the accompanying Tables as well as in the Examples.

Preferably, the reference, that is, the values for at least one characteristic feature of the at least one biomarker or its relationships, will be stored in a suitable data storage medium such as a database and are therefore also available for Future evaluations

The term "compare" refers to determining whether the determined value of a biomarker is essentially identical to a reference or differs from the previous one. Preferably, a biomarker value is considered to differ from a reference if the difference observed is statistically significant, which can be determined by the statistical techniques referred to elsewhere in this description. If the difference is not statistically significant, the value of the biomarker and the reference are essentially identical. Based on the comparison referred to above, it can be assessed that a subject suffers from the disease, or not.

For the specific biomarkers referred to in this specification, the preferred values for changes in relative quantities or ratios (i.e., changes expressed as the relationships between means) or the type of regulation (ie "increasing" or "decreasing" regulation or the resulting increase or decrease in a greater or lesser amount or relative and / or absolute ratio) are indicated in the Tables and in the following Examples. The relationship between the means indicates the degree of increase or decrease, for example, a value of 2 means that the amount is twice the amount of the biomarker compared to the reference. In addition, this is evident if there is a "growing regulation" or a "decreasing regulation". In the case of a "growing regulation", the average proportion should exceed 1.0 while it will be below 1.0 in the case of a decreasing regulation. Therefore, the direction of regulation can also be derived from the Tables. It will be understood that instead of the means, the medians could also be used.

Preferably, the values or ratios determined in a sample of a subject according to the present invention are adjusted for age, BMI, gender or other existing diseases, for example, the presence or absence of diabetes before being compared with a reference . Alternatively, references can be derived from values or relationships that have also been adjusted for age, BMI, gender or other diseases, for example, the presence or absence of diabetes. Said adjustment can be made by deriving the references and the underlying values or relationships of a group of subjects to individual subjects that are essentially identical with respect to these parameters in the subject to be investigated. As an alternative, the adjustment can be carried out using statistical calculations.

Preferably, if a reference that has been obtained from a subject or group of subjects known to have heart failure is applied, an increase in the amount of at least one biomarker should be indicative of the heart failure of a selected biomarker a from the related biomarkers in any one of Tables 1A1, 1B1, 2A1, 2B1, 3A1,3B1, 4A1, 4B1, 5A1, 5B1, 6A1, 6B1, 7A1, 7B1, 8A1 or 8B1. Preferably, if a reference that has been obtained from a subject or group of subjects known to have heart failure is applied, a decrease in the amount of at least one biomarker should be indicative of the heart failure of a biomarker selected from related biomarkers in any one of Tables 1A2, 1B2, 2A2, 2B2, 3A2, 3B2, 4A2, 4B2, 5A2, 5B2, 6A2, 6B2, 7A2, 7B2, 8A2, or 8B2.

The comparison is preferably assisted by automation. For example, a suitable computer program comprising algorithms for the comparison of two different data sets (for example, data sets comprising characteristic feature values (s)) can be used. Such computer programs and algorithms are well known in the art. Despite the above, a comparison can be carried out manually.

Advantageously, it has been found in the study on which the present invention underlies that the specific biomarkers referred to above are indicators of heart failure. Therefore, the at least one biomarker as specified above in a sample can, in principle, be used to assess whether a subject suffers from heart failure. This is particularly useful for an effective diagnosis of the disease as well as to improve the preclinical and clinical management of heart failure as well as an effective monitoring of patients. In addition, the findings underlying the present invention will facilitate the development of effective drug-based treatments or other interventions that include nutritious diets against heart failure as shown in detail below.

The definitions and explanations of the terms made above are applied analogously making the necessary changes for the following embodiments of the present invention unless otherwise specified herein below.

The present invention also relates to a method for identifying if a subject is in need of a heart failure treatment comprising the steps of the procedure of claim 1 and the additional step of identifying a subject that needs to be diagnosed with heart failure. .

The expression "in need of a treatment for heart failure" as used herein means that the disease in the subject is in a state in which therapeutic intervention is necessary or beneficial in order to improve or treat heart failure. or the symptoms associated with it. Therefore, the findings of the studies underlying the present invention not only allow diagnosing heart failure in a subject but also identify subjects that should be treated by a treatment for heart failure or whose treatment for heart failure needs adjustment . Once the subject has been identified, the procedure may also include a step of making recommendations for a treatment of heart failure.

A treatment of heart failure as used in accordance with the present invention preferably refers to a treatment comprising or consisting of the administration of at least one drug selected from the group consisting of: ACE inhibitors (ACEI), Beta blockers, ATI inhibitors, Aldosterone antagonists, Renin antagonists, Diuretics, Ca sensitizers, Digitalis glycosides, S100 protein family polypeptides (as disclosed in documents DE000003922873A1, DE000019815128A1 or DE000019915485A1 peptides) as BNP (Nesiritide (recombinant human brain natriuretic peptide - BNP)) or Np.

The present disclosure further relates to a procedure for determining whether a treatment against heart failure is satisfactory in a subject comprising the steps of the procedures of the present invention and the additional step of determining whether a treatment is satisfactory if insufficiency is not diagnosed. cardiac

It should be understood that a treatment of heart failure will be satisfactory if heart failure, or at least some of its symptoms can be treated or improved compared to an untreated subject. In addition, a treatment is also satisfactory, as understood herein if disease progression can be prevented or at least delayed compared to an untreated subject.

The present invention also relates to a method of controlling the progression or regression of heart failure in a subject according to claim 12.

The present disclosure also relates to a procedure for monitoring the progression or regression of heart failure in a subject, comprising the steps of:

a) determine in a first and second sample of said subject the amount of at least one biomarker selected from the related biomarkers in Table 9A1, 9A2, 9B1, 9B2, 10A1, 10A2, 10B1, 10B2, 11A1, 11A2, 11B1, 11B2, 12A1, 12A2, 12B1, 12B2, 13A1, 13A2, 13B1 or 13B2 in which said first sample was obtained before said second sample; Y

c) compare the amount determined in the first sample with the amount determined in the second sample, for which the progression or regression of heart failure is to be diagnosed.

The term "monitor", as used herein, refers to determining the progression of heart failure or the regression of heart failure between the time point when the first sample has been taken to the time point when it has been taken. The second sample. Surveillance can also be used to determine if a patient has been treated satisfactorily or if at least the symptoms of heart failure can be improved over time by a certain treatment.

The term "progression", as used herein refers to the worsening of heart failure or its accompanying symptoms. Preferably, the progression, as referred to herein, refers to a progression of asymptomatic to symptomatic heart failure and, more preferably, from NYHA class I to NYHA class III. Similarly, the term "regression" as used herein, refers to an improvement in heart failure or its accompanying symptoms. Preferably, the regression, as referred to herein, refers to a regression of symptomatic to asymptomatic heart failure and, more preferably, from NYHA class III to NYHA class I or even a healthy state . It should be understood that a regression of heart failure, preferably, occurs after the application of a heart failure treatment as specified elsewhere herein. Accordingly, the aforementioned procedure may also preferably be applied in order to determine whether a treatment against heart failure is satisfactory in a subject.

Preferably, the heart failure is DCMP and said at least one biomarker is selected from the related biomarkers in Table 10A1, 10A2, 10B1 or 10B2. Also preferably, said heart failure is ICMP and said at least one biomarker is selected from the related biomarkers in Table 11A1, 11A2, 11B1 or 11B2. More preferably, said heart failure is HCMP and said at least one biomarker is selected from the related biomarkers in Table 12A1, 12A2, 12B1 or 12B2.

More preferably, if a blood plasma or serum sample is applied, the at least one biomarker is selected from the related biomarkers in any one of Tables 9A1, 9A2, 10A1, 10A2, 11A1, 11A2, 12A1, 12A2, 13A1 or 13A2 . More preferably, if a urine sample is applied, the at least one biomarker is selected from the related biomarkers in any one of Tables 9B1, 9B2, 10B1, 10B2, 11B1, 11B2, 12B1, 12B2, 13B1 or 13B2.

Preferably, an increase in the amount of at least one biomarker determined in the second sample compared to the first sample should be indicative of heart failure for a biomarker selected from the related biomarkers in any one of Tables 9A1, 9B1, 10A1, 10B1, 11A1, 11B1, 12A1 or 12B1. Preferably, a decrease in the amount of at least one biomarker determined in the second sample compared to the first sample should be indicative of heart failure for a biomarker selected from the related biomarkers in any one of Tables 9A2, 9B2, 10A2, 10B2, 11A2, 11B2, 12A2 or 12B2.

Also preferably, said progression or regression of heart failure surveillance is accompanied by the progression or regression of a reduced left ventricular ejection fraction if the amount of at least one biomarker selected from the related biomarkers in Table 13A1, 13A2 , 13B1, or 13B2 are determined in the above-mentioned surveillance procedure.

A reduced LVEF as it is called according to the aforementioned procedure is, preferably, a significantly reduced LVEF as specified elsewhere herein. Preferably, the subject to be investigated by the aforementioned procedure is a subject suffering from, or is suspected of suffering from, DCPM and / or ICPM.

More preferably, if a blood plasma or serum sample is applied, the at least one biomarker is selected from the related biomarkers in any one of Tables 13A1 or 13A2. More preferably, if a urine sample is applied, the at least one biomarker is selected from the related biomarkers in any one of Tables 13B1 or 13B2.

Preferably, an increase in the amount of at least one biomarker determined between the second sample compared to the first sample should be indicative of the progression of heart failure for a biomarker selected from the related biomarkers in any one of Tables 13A2 or 13B2. Preferably, a decrease in the amount of at least one biomarker determined in the second sample compared to the first sample should be indicative of the progression of heart failure for a biomarker selected from the related biomarkers in any one of Tables 13A1 or 13B1 . Preferably, the comparison between the first and the second sample as called in accordance with the above-mentioned procedure is carried out by calculating the average proportions as an indicator of the force and the direction of regulation of a given biomarker. The average proportions were calculated by dividing the average of the amount of the biomarker in the heart failure group by the mean of reference, that is, the average amount of the biomarker in a group of reference subjects. The results that relate to the correlation of metabolite levels with LVEF are described by statistical parameters, such as the p-value, for the correlation and the estimate that indicate the slope of the regression line in standard deviation units for the respective metabolites .

The above-mentioned procedures for the determination of at least one biomarker can be implemented in a device. A device as used herein should comprise at least the aforementioned means. In addition, the device preferably also comprises means for comparing and evaluating the characteristic trait (s) detected of at least one biomarker and, also preferably, the intensity of the determined signal. The device means are preferably operatively linked together. How to link the media in an operational manner will depend on the type of media included in the device. For example, when means for qualitatively or quantitatively determining the biomarker are applied automatically, the data obtained by said operative means can automatically be processed by, for example, a computer program in order to facilitate the evaluation. Preferably, the means are comprised of a single device in said case. Said device may according to them include an analysis unit for the biomarker and a computer unit for processing the data resulting from the evaluation. Preferred devices are those that can be applied without the specific knowledge of a specialized physician, for example, electronic devices that merely require loading with a sample. Alternatively, the procedures for determining at least one biomarker can be implemented in a system comprising some devices that are preferably operatively linked to each other. Specifically, the means must be joined in a manner that allows the process of the present invention to be carried out as described in detail above. Therefore, operably linked, as used herein, preferably means functionally linked. Depending on the means to be used for the system of the present invention, said means can be functionally linked by connecting each medium with the other by means that allow the transport of data between said means, for example, fiberglass cables, and others. High performance data transport cables. However, wireless data transfer between the media is also contemplated by the present invention, for example, via LAN (wireless LAN, W-LAN). A preferred system comprises the means for determining biomarkers. Means for determining biomarkers as used herein encompass means for separating biomarkers, such as chromatographic devices, and means for determining metabolites, such as mass spectrometry devices. Suitable devices have been described in detail above. Preferred means for separating the compound to be used in the system of the present invention include chromatographic devices, more preferably, devices for liquid chromatography, HPLC, and / or gas chromatography. Preferred devices for compound determination comprise mass spectrometry devices, more preferably, GC-MS, LC-MS, direct infusion mass spectrometry, FT-ICR-MS, CE-MS, HPLC-MS, mass spectrometry in quadrupole, sequentially coupled mass spectrometry (MS-MS or MS-MS-MS), ICP-MS, Py-MS or TOF. The separation and determination means are preferably coupled to each other. Most preferably, LC-MS and / or GC-MS are used in the system of the present invention as described in detail elsewhere in the specification. The means for comparing and / or analyzing the results obtained from the means for the determination of biomarkers must also be included. The means for comparing and / or analyzing the results may further comprise at least one database and a computer program implemented for the comparison of the results. The preferred embodiments of the aforementioned systems and devices are also described in detail below.

Therefore, the present invention relates to a diagnostic device according to claim 11.

The present disclosure refers to a diagnostic device comprising:

a) an analysis unit comprising a detector for at least one biomarker as related in any one of Tables 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 7B2, 8A1, 8A2, 8B1, 8B2, 9A1, 9A2, 9B1, 9B2, 10A1, 10A2, 10B1, 10B2, 11A1, 11A2, 11B1, 11B2, 11B1, 11B2, 11B1, 11B2 12A1, 12A2, 12B1, 12B2, 13A1, 13A2, 13B1 or 13B2, wherein said analysis unit is adapted to determine the amount of said biomarker detected by the detector, and, operatively linked to the previous one;

b) an evaluation unit comprising a computer, comprising a tangible computer program code included to carry out a comparison of the determined amount of the at least one biomarker and a reference amount and a database comprising said reference amount as for the mentioned biomarker so it will be diagnosed if a subject suffers from heart failure. Preferably, the computer program code is capable of executing the step of the process of the present invention as specified in detail elsewhere herein.

In a preferred embodiment, the device comprises a database comprising the type of regulation and / or the level of the regulation values indicated for the respective at least one biomarker in any one of Tables 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A, 7B2, 8A 8A2, 8B1, 8B2, 9A1, 9A2, 9B1, 9B2, 10A1, 10A2, 10B1, 10B2, 11A1, 11A2, 11B1, 11B2, 12A1, 12A2, 12B1, 12B2, 13A1, 13A2, 13B1 or 13B2 and a program code information included in a tangible way to additionally carry out a comparison between the specific type of regulation and / or the level of the regulation values and those included in the database.

Further, the present invention relates to a data storage medium comprising a data collection according to claim 10.

The present disclosure refers to a collection of data comprising characteristic values of at least one biomarker that are indicative of a medical condition or effect, as shown above (ie, diagnosing heart failure in a subject).

The term "data collection" refers to a data collection that can be grouped together physically and / or logically. Accordingly, data collection can be implemented in a single data storage medium or in physically separate data storage media that are operatively linked to each other. Preferably, data collection is implemented by means of a database. Therefore, a database, as used herein, comprises the collection of data in a suitable storage medium. In addition, the database preferably also comprises a database management system. The database management system is preferably the object-based or hierarchical network-based database management system. In addition, the database can be a federal or integrated database. More preferably, the database will be implemented as a distributed (federal) system, for example, as a client-server system. More preferably, the database is structured in such a way that it is possible to search for an algorithm to compare a set of test data with the data sets comprised by data collection. Specifically, using said algorithm, similar or identical data sets that are indicative of a medical condition or effect can be investigated in the database as shown above (for example, a search for a query). Therefore, if an identical or similar data set can be identified in the data collection, the test data set will be associated with the said medical condition or effect. Accordingly, the information obtained from data collection can be used, for example, as a reference for the methods of the present invention described above. More preferably, the data collection comprises characteristic values of all biomarkers comprised of any one of the groups listed above.

In the light of the foregoing, the present disclosure encompasses a data storage medium comprising the aforementioned data collection.

The term "data storage medium", as used herein, encompasses data storage media that are based on individual physical entities such as CD, a CD-ROM, a hard disk, optical storage media, or a floppy disk In addition, the term further includes data storage means consisting of physically separate entities that are operatively linked to each other in such a way as to provide the aforementioned data collection, preferably, in a manner suitable for searching a query.

The present disclosure also refers to a system comprising:

(a) means for comparing characteristic values of at least one biomarker of a sample operatively linked to

(b) a data storage medium as described above.

The term "system" as used herein refers to different means that are linked Operationally with each other. The mentioned means can be implemented in a single device or they can be physically separated devices that are operatively linked to each other. The means for comparing biomarker characteristic values, preferably, are based on an algorithm for comparison, as mentioned above. The data storage medium preferably comprises the data collection or the aforementioned database, each of the data sets being indicative of a medical condition or medical condition referred to above. Therefore, the system of the present invention makes it possible to identify whether a set of test data is comprised of the collection of data stored in the data storage medium. Accordingly, the methods of the present invention can be implemented by the system of the present invention.

In a preferred disclosure of the system, the means for determining the characteristic values of the biomarkers of a sample are included. The term "means for determining the characteristic values of biomarkers" preferably refers to the aforementioned devices for the determination of metabolites such as mass spectrometry devices, NMR devices or devices for carrying out chemical or biological tests for Biomarkers

In addition, the present disclosure relates to diagnostic means comprising means for the determination of at least one biomarker selected from any one of the groups referred to above.

The term "diagnostic means" preferably refers to a biological or chemical diagnostic device, system or test as specified elsewhere in the detailed description.

The expression "means for the determination of at least one biomarker" refers to devices or agents that are capable of specifically recognizing the biomarker. Suitable devices may be spectrometric devices such as mass spectrometry, NMR devices or devices for carrying out chemical or biological tests for biomarkers. Suitable agents may be compounds that specifically detect biomarkers. The detection as used herein may be a two-stage procedure, that is, the compound may first be specifically linked to the biomarker to be detected and subsequently generate a detectable signal, for example, fluorescent signals, chemiluminescent signals, radioactive signals and the like. For the generation of the detectable signal, additional compounds may be required which are comprised of the term "means for the determination of at least one biomarker". Compounds that specifically bind to the biomarker are described elsewhere in the specification in detail and preferably include enzymes, antibodies, ligands, receptors or other biological molecules or chemical compounds that specifically bind to biomarkers.

In addition, the present disclosure relates to a diagnostic composition comprising at least one biomarker selected from any one of the groups mentioned above.

The at least one biomarker selected from any of the aforementioned groups will serve as a biomarker, that is, an indicator molecule for a medical condition or effect on the subject as defined elsewhere in this document. Therefore, biomarker molecules themselves can serve as diagnostic compositions, preferably, after visualization or detection by the means referred to herein. Therefore, a diagnostic composition indicating the presence of a biomarker according to the present invention can also physically comprise said biomarker, for example, a complex of an antibody and the biomarker to be detected can serve as the diagnostic composition. . Accordingly, the diagnostic composition may further comprise means for the detection of metabolites, as specified elsewhere in the present description. Alternatively, if detection means such as techniques based on MS or NMR are used, the molecular species that serves as an indicator for risk ailment will be at least one biomarker comprised of the test sample to be investigated. Therefore, the at least one biomarker referred to in accordance with the present invention must in itself serve as a diagnostic composition due to its identification as a biomarker.

The present invention further relates to a use according to claim 8.

In general, the present disclosure relates to the use of at least one biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2 , 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1 or 8B2 in a sample of a subject to diagnose heart failure.

Preferably, said subject suffers from asymptomatic heart failure, preferably according to NYHA class I and the at least one biomarker is a biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1, 2A2 , 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1 or 4B2. In particular, in the use of the present invention, asymptomatic heart failure is (I) DCPM and said at least one biomarker is selected from the related biomarkers in Table 2A1, 2A2, 2B1 or 2B2, (ii) ICMP and said at At least one biomarker is selected from the related biomarkers in Table 3A1, 3A2, 3B1 or 3B2 or (iii) HCPM and said at least one biomarker is selected from the related biomarkers in Table 4A1, 4A2, 4B1 or 4B2.

Furthermore, it is preferably contemplated in accordance with the use of the present disclosure, that said heart failure is a symptomatic heart failure, preferably according to NYHA class II and / or III and that at least one biomarker is a selected biomarker. among the related biomarkers in Table 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1 or 8B2. In particular, in the use of the present invention, symptomatic heart failure is (i) DCPM and said at least one biomarker is selected from the related biomarkers in Table 6A1, 6A2, 6B1 or 6B2, (ii) ICMP and said at At least one biomarker is selected from the related biomarkers in Table 7A1, 7A2, 7B1 or 7B2 or (iii) HCPM and said at least one biomarker is selected from the related biomarkers in Table 8A1, 8A2, 8B1 or 8B2.

Finally, the present disclosure contemplates the use of at least one biomarker selected from the related biomarkers in Table 9A1, 9A2, 9B1, 9B2, 10A1, 10A2, 10B1, 10B2, 11A1, 11A2, 11B1, 11B2, 12A1, 12A2, 12B1 , 12B2, 13A1, 13A2, 13B1 or 13B2 in a sample of a subject to monitor the progression or regression of heart failure.

Preferably, the subject suffers from DCM and / or ICPM. Also preferably, at least one biomarker selected from the related biomarkers in Table 13A1, 13A2, 13B1 or 13B2 can be used in a sample of said subject to determine the progression or regression of a reduced LVEF in the subject.

Examples

The invention will now be illustrated by the following Examples that are not intended to restrict or limit the scope of the present invention.

Example 1: Study design for the differentiation of CHF subtypes of DCMP (dilated cardiomyopathy), ICMP (ischemic cardiomyopathy) and HCMP (hypertrophic cardiomyopathy) from healthy controls.

The study included 81 patients, men and women with DCMP, 81 patients, men and women with ICMP, and 80 patients, men and women with HCMP, as well as 83 healthy controls, men and women aged 35-75 and a BMI of 20 -35 kg / m2. The NYHA (New York Heart Association) scores of patients varied between 1-3. Patients and controls were matched for age, gender and BMI. For all patients and controls, a blood sample and a urine sample were collected. Plasma was prepared by centrifugation, and the samples were stored at -80 ° C until the measurement was carried out.

Three subgroups of CHF (DCMP, ICMP and HCMP) were defined based on echocardiographic and hemodynamic criteria:

a) DCMP subgroup: hemodynamically defined as a failure in systolic pumping with cardiomegaly (echocardiographic improvement of the final diastolic diameter of the left ventricle> 55 mm and a restricted left ventricular ejection fraction - LVEF of <50%).

b) ICMP subgroup: hemodynamically defined as a failure in systolic pumping due to coronary insufficiency (> 50% coronary stenosis and stress-inducible endocardial motor insufficiency as well as an LVEF of <50%)

c) Subgroup of HCMP: concentric cardiac hypertrophy (echocardiography - septum> 11 mm, posterior myocardial wall> 11 mm) and with a diastolic CHF (without pumping function or with intermediate pumping function with an LVEF of> 50%).

Class IV NYHA patients were excluded as well as patients suffering from stroke, patients who had had myocardial infarction in the last 4 months before the trial, patients with altered medications at least 4 weeks before the trial as well as patients suffering from acute or chronic inflammatory diseases and malignant tumors.

Example 2: Determination of metabolites

Human plasma samples were prepared and subjected to LC-MS / MS and GC-MS or SPE-LC-MS / MS (hormones) as described below:

Proteins were separated by precipitation from blood plasma. After the addition of water and a mixture of ethanol and dichloromethane, the remaining sample was fractionated in an aqueous solar phase, and an organic lipophilic phase.

For the transmetanolysis of the lipid extracts, a mixture of 140 pl of chloroform, 37 pl of hydrochloric acid (37% by weight of HCl in water), 320 pl of methanol and 20 pl of toluene were added to the evaporated extract. The vessel was tightly sealed and heated 2 hours at 100 ° C, with stirring. The solution was subsequently evaporated to dryness. The residue was dried completely.

The methoximation of the carbonyl groups was carried out by reaction with methoxyamine hydrochloride (20 mg / ml in pyridine, 100 l for 1.5 hours at 60 ° C) in a tightly sealed container. 20 pl of a linear chain fatty acid solution of odd numbers was added (solution of every 0.3 mg / ml of fatty acids of 7 to 25 carbon atoms and every 0.6 mg / ml of fatty acids with 27 , 29 and 31 carbon atoms in 3/7 (v / v) pyridine / toluene) as temporary standards. Finally, derivatization was carried out with 100 pl of N-methyl-N (trimethylsilyl) -2,2,2-trifluoroacetamide (MSTFA) for 30 minutes at 60 ° C, again in the tightly closed container. The final volume before injection into the GC was 220 pl.

For the polar phase, derivatization was carried out as follows: The carbonyl groups were approximated by reaction with methoxyamine hydrochloride (20 mg / ml in pyridine, 50 l for 1.5 hours at 60 ° C) in a tightly sealed container. 10 pl of a linear chain fatty acid solution of odd numbers were added (solution of every 0.3 mg / ml of fatty acids of 7 to 25 carbon atoms and every 0.6 mg / ml of fatty acids with 27 , 29 and 31 carbon atoms in 3/7 (v / v) pyridine / toluene) as temporary standards. Finally, derivatization was carried out with 50 pl of N-methyl-N- (trimethylsilyl) -2,2,2-trifluoroacetamide (MSTFA) for 30 minutes at 60 ° C, again in the tightly closed container. The final volume before injection into the GC was 110 pl.

GC-MS systems consist of an Agilent 6890 GC coupled to an Agilent 5973 MSD. The autosamplers are CompiPal or GCPal from CTC.

For the usual analysis, commercial capillary separation columns (30 mx 0.25 mm x 0.25 pm) were used with different stationary phases of polymethyl siloxane containing 0% to 35% aromatic residues, depending on the materials and analyzed sample fractions from the phase separation stage, (for example: DB-1 ms, HP-5ms, DB-XLB, DB-35ms, Agilent Technologies). Up to 1 pl of the final volume without divisions was injected and the oven temperature program was started at 70 ° C and finished at 340 ° C with different heating rates depending on the material and sample fraction from the phase separation stage in order to achieve a sufficient chromatographic separation and a number of scans at each analyte peak. In addition, RTL (Retention Time Blocking, Agilent Technologies) was used for the usual standard GC-MS analysis and conditions, for example, constant flow with a nominal flow of 1 to 1.7 ml / min and helium as the gas of the mobile phase, the ionization was carried out by impact of electrons with a scan of 70 eV, in a range of m / z from 15 to 600 with scanning speeds of 2.5 to 3 scans / s and conditions of standardized settings.

HPLC-MS systems consisted of an Agilent 1100 LC system (Agilent Technologies, Wald-bronn, Germany) coupled with an API 4000 mass spectrometer (Applied Biosystem / MDS SCIEX, Toronto, Canada). HPLC analysis was performed on commercially available reverse phase separation columns with C18 stationary phases (eg, GROM ODS 7 pH, Thermo Betasil C18). Up to 10 pl of the final volume of the evaporated and reconstituted lipophilic and polar phase sample were injected and separation was carried out with gradient elution using methanol / water / formic acid or acetonitrile / water / formic acid gradients at a flow rate 200 pl / min

Mass spectrometry was carried out by electrospray ionization in positive mode for the non-polar fraction and negative or positive mode for the polar fraction using the multiple reaction monitoring mode (MRM) and full scanning from 100-1000 amu.

Steroids and their metabolites were measured by SPE-LC-MS (solid phase extraction-LC-MS). Catecholamines and their metabolites were measured by SPE-LC-MS online as described by Yamada et al. (J. Anal.Toxicol. (26), 2002, 17-22). For catecholamines and related metabolites and steroids and related metabolites, quantification was achieved by means of standards marked with stable isotopes, and absolute concentrations were calculated.

Analysis of complex lipids in plasma samples:

Total lipids were extracted from plasma by liquid / liquid extraction using chloroform / methanol. The lipid extracts were subsequently fractionated by normal phase liquid chromatography (NPLC) into eleven different lipid groups according to Christie (Journal of Lipid Research (26), 1985, 507-512).

The fractions were analyzed by LC-MS / MS using electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) with detection of specific multiple reaction surveillance (MRM) transitions for cholesterol esters (CE), sterols free (ES), sphingomyelins (SM) and ceramides (CER) respectively. Sphingosines and sphingosine-1-phosphates (SP) were analyzed by LC-MS / MS using electrospray ionization (ESI) with detection of specific multiple reaction surveillance (MRM) transitions as described by Schmidt H et.al ., Prostaglandins & other Lipid Mediators 81 (2006), 162 170. The metabolites in the following tables are derived from one of these fractions that include the respective abbreviation in their name.

Monoacylglyceride (MAG), triacylglyceride (TAG), phosphatidylcholines (PC), phosphatidylserines (PS), phosphatidylinositols (PI), lysophosphatidylcholine (LPC), diacylglycerols (DAG), free fatty acids (FFA) lipid classes were measured MS.

The fractions were analyzed by GC-MS after derivatization with TMSH (Trimethyl Sulfonium Hydroxide), resulting in the fatty acid methyl esters (FAME) corresponding to the acyl moieties of the lipids separated into classes. FAME concentrations from C14 to C24 in each fraction were determined.

The metabolites in the following Tables are derived from one of these fractions that include the respective abbreviation in front of the name separated by a underscore.

Eicoisanoids and related metabolites were measured outside the plasma by means of SPE LC-MS / MS offline and online (Solid Phase Extraction-LC-MS / MS) (Masoodi M and Nicolaou A: Rapid Commun Mass Spectrom.

2006; 20 (20): 3023-3029. Absolute quantification was carried out by means of patterns marked with stable isotopes.

Samples from all patients were subjected to the full spectrum of analysis of the procedure for metabolite profiles as described above, with the exception of the metabolite profile of the plasma polar phase by LC-MS / MS using the ionization mode by positive electrospray that was applied to a subset of 75 samples comprising controls, NYHA class I patients with DCPM, NYHA class III patients with DCPM, NYHA class II patients with HCPM and NYHA class III patients with ICPM.

Human urine samples were prepared and subjected to the analysis of LC-MS / MS and GC-MS or SPE-LC-MS / MS (hormones) as described below:

Degradation of urea was carried out by reaction with urease for 1 h at 30 ° C. After the addition of methanol, the extract was evaporated to dryness.

Derivatization as follows: The carbonyl groups were approximated by reaction with methoxyamine hydrochloride (20 mg / ml in pyridine, 50 l for 1.5 hours at 60 ° C) in a tightly sealed container. 10 pl of a linear chain fatty acid solution of odd numbers were added (solution of every 0.3 mg / ml of fatty acids of 7 to 25 carbon atoms and every 0.6 mg / ml of fatty acids with 27 , 29 and 31 carbon atoms in 3/7 (v / v) pyridine / toluene) as temporary standards. Finally, derivatization was carried out with 50 pl of N-methyl-N- (trimethylsilyl) -2,2,2-trifluoroacetamide (MSTFA) for 30 minutes at 60 ° C, again in the tightly closed container. The final volume before injection into the GC was 110 pl.

GC-MS systems consist of an Agilent 6890 GC coupled to an Agilent 5973 MSD. The autosamplers are CompiPal or GCPal from CTC.

For the usual analysis, commercial capillary separation columns (30 mx 0.25 mm x 0.25 pm) were used with different stationary phases of polymethyl siloxane containing 0% to 35% aromatic residues, depending on the materials and analyzed sample fractions from the phase separation stage, (for example: DB-1 ms, HP-5ms, DB-XLB, DB-35ms, Agilent Technologies). Up to 1 pl of the final volume without divisions was injected and the oven temperature program was started at 70 ° C and finished at 340 ° C with different heating rates depending on the material and sample fraction from the phase separation stage in order to achieve a sufficient chromatographic separation and a number of scans at each analyte peak. In addition, RTL (Retention Time Blocking, Agilent Technologies) was used for the usual standard GC-MS analysis and conditions, for example, constant flow with a nominal flow of 1 to 1.7 ml / min and helium as the gas of the mobile phase, the ionization was carried out by impact of electrons with a scan of 70 eV, in a range of m / z from 15 to 600 with scanning speeds of 2.5 to 3 scans / s and conditions of standardized settings.

HPLC-MS systems consisted of an Agilent 1100 LC system (Agilent Technologies, Wald-bronn, Germany) coupled with an API 4000 mass spectrometer (Applied Biosystem / MDS SCIEX, Toronto, Canada). HPLC analysis was performed on commercially available reverse phase separation columns with C18 stationary phases (eg, GROM ODS 7 pH, Thermo Betasil C18). Up to 10 pl of the final volume of the evaporated and reconstituted lipophilic and polar phase sample were injected and separation was carried out with gradient elution using methanol / water / formic acid or acetonitrile / water / formic acid gradients at a flow rate 200 pl / min

Mass spectrometry was carried out by electrospray ionization in negative or positive mode using the multiple reaction monitoring mode (MRM) and full scanning from 100-1000 amu. Steroid deconjugation was carried out using a glucuronidase / sulfatase mixture. Steroids and their metabolites were measured by SPE-LC-MS (solid phase extraction-LC-MS). Catecholamines and their metabolites were measured by SPE-LC-MS online as described by Yamada et al. (J. Anal.Toxicol. (26), 2002, 17-22). For catecholamines and related metabolites and steroids and related metabolites, quantification was achieved by means of standards marked with stable isotopes.

Example 3: Data analysis and statistical evaluation

Plasma and urine samples were analyzed in a randomized analytical sequence design with combined samples (called "combined") generated from aliquots of each sample. After the comprehensive analytical validation stages, the raw peak data of each analyte was normalized to the median of the analytical sequence by combined to take into account the variability of the procedure (thus referred to as "normalized ratios for the combined"). If available, concentrations were used Absolute metabolites for statistical analysis. In all other cases, normalized relationships were used for the combination. All data was transformed for log10 to achieve normal distribution.

The study described in Example 1 was analyzed using an ANOVA model comprising age factors, BMI, gender (including all binary interactions), diagnostic group and storage time (optional). The levels for the diagnostic group of the factor were the subtype / grade CHF (DCMP NYHA I, DCMP nYhA M-In, ICMP NYHA I, ICMP NYHA N-NI, HCMP NYHA I, HCMP NYHA II-III) and control (adjusted as reference). The corresponding results are listed in Tables 1 to 8.

To identify the biomarkers of CHF progression as expressed by the NYHA stage, this analysis was refined using the following levels for the diagnostic group: (DCMP NYHA I, CHF NYHA II-III, controls (adjusted for reference). Specific markers of disease progression subtype, this analysis was refined using the following levels for the diagnostic group: DCMP NYHA I, DCMP NYHA II, DCMP NYHA III, HCMP NYHA I, HCMP NYHA II, HCMP NYHA III, ICMP NYHA I, ICMP NYHA II, ICMP NYHA III, control (adjusted for reference) An appropriate biomarker to monitor disease progression was defined as much as increased by> = 10% in NYHA group I compared to controls, and in a further increase> = 10% of NYHA I to NYHA III, or decreasing by more than 10% in NYHA group I compared to controls and an additional decrease of more than 10% from NYHA I to NYHA III In addition, the metabolites are sealed they were only listed as progression monitoring biomarkers if the p-value for at least two of the following three comparisons was <0.05: NYHA I vs. control, NYHA II vs. control, NYHA III vs. control.

To identify the markers of metabolite progression as defined in the left ventricular ejection fraction (LVEF), the numerical values of LVEF were used for correlation with metabolite data. The LVEF correlation of a data set comprising patients with DCPM and patients with ICPM as well as controls was carried out (Note: HCPM by definition in this study, and in contrast to DCPM and ICPM, is not characterized by a decrease in the LVEF).

In the following tables, the average proportion indicates the force and direction of the regulation. The average proportion was calculated by dividing the average metabolite level in the CHF group by the average metabolite level in the healthy control group. The results that refer to the correlation of metabolite levels with LVEF are described by the p-value of the correlation and an estimate that indicates the slope of the regression line expressed in units of standard deviation for the respective metabolites.

The biomarkers to be determined in accordance with the methods of the present invention are listed in the following tables. Biomarkers are not defined precisely by name and are further characterized in an additional table, below.

Table 1A.1: Metabolites that are significantly increased in plasma (p value <0.05) among all asymptomatic CHF patients with NYHA I and controls

Metabolite ratio regulation value p

Ribonic acid 1.1532 up to 0.003128 Maltose 1,846 up to 1,72E-09 Serotonin (5-HT) 1,6463 up to 0,007114 Sorbitol 1,624 up to 1,83E-05 Fructose 1,5213 up to 0,000054

12-hydroxyeicosatetraenoic acid (C20: cis [5,8,10,14] 4) 1,4962 to 0.002721

TAG (C16: 0, C18: 1, C18: 2) 1.4229 to 2.68E-05

GAD (C18: 1, C18: 2) 1,4001 to 5.46E-06 Glycerol, lipid fraction 1.3518 to 0.000456

TAG (C16: 0, C16: 1) 1.3469 to 0.000472

GAD (C16: 0, C18: 2) 1,3372 to 1.22E-05 Sucrose 1.3313 to 0.018153 Glutamate 1.3299 to 5.61 E-05 Noradrenaline (norepinephrine) 1.2923 to 0.000106 Normetanephrine 1,282 to 0,003054

15-hydroxyeicosatetraenoic acid (C20: cis [5,8,11,13] 4) 1,2805 to 0.001914

TAG (C18: 2, C18: 2) 1.2768 to 0.001589

Lauric acid (C12: 0) 1,2759 to 0.039117 Sphingosine (d16: 1) 1,262 to 0.001212 Sphingosine (d18: 1) 1,2556 to 0.007386 Taurine 1.2546 to 3.79E-06 Sphingadienin ( d18: 2) 1.2362 to 0.011055 Sphinganine (d18: 0) 1.2346 to 0.002921

TAG (C16: 0, C18: 1, C18: 3) 1,2262 to 0.009708

(continuation)

Metabolite ratio regulation value p

8-hydroxyeicosatetraenoic acid (C20: trans [5] 1,2242 to 0.009653 cis [9,11,14] 4) (8-HETE)

Eicosenoic acid (C20: cis [11] 1) 1.2156 to 0.000987 DAG (C18: 1, C18: 2) 1.1933 to 0.006155

3-Methoxytyrosine 1.1923 up to 0.002251 Pyruvate 1.1835 up to 0.016371 Cystine 1.1776 up to 0.001235 isocitrate 1.1608 up to 0.000133 Oleic acid (C18: cis [9] 1) 1.1584 up to 0 , 015278 14,15-Dihydroxyeicosatrienoic acid (C20: cis [5,8,11] 3) 1,1535 to 0.023326 Eritrol 1,1357 to 0.003593 Uric acid 1.1348 to 0.00076 Glucosamine 1,1265 to 0.034382 alpha-ketoglutarate 1,1241 to 0.010357 Isoleucine 1,1198 to 0.000476 Glycerol-3-phosphate, polar fraction 1.1076 to 0.043225 Glucose-1-phosphate 1.1066 to 0.005294 mio- Inositol 1.1062 to 0.003496 Alanine 1.1026 to 0.006338 Proline 1.0985 to 0.038074 Sarcosine 1.0973 to 0.000248 Tyrosine 1.0909 to 0.008773 Arginine 1.0855 to 0.040093 Cysteine 1.085 up to 0.002133 Aspartate 1.0844 up to 0.027374 Ornithine 1.0842 up to 0.010899 Pseudouridine 1.0826 up to 0.011665 Glucose 1.0796 up to 0.001303 Phenylalanine 1.0619 up to 0.016262 Phosphatidylcholine (C18: 0, C18: 2) 1.0105 to 0.005529

Table 1A.2: Metabolites that are significantly decreased in plasma (p value <0.05) among all asymptomatic CHF patients with NYHA I and controls

Metabolite regulation ratio p-value Cholesterilester C18: 2 0.7014 to 4.82E-11 Beta-carotene 0.7099 to 3.98E-05 Dehydroepiandrosterone sulfate 0.7248 to 0.007073

CE-Cholesterilester C12: 0 0.7325 to 0.008196

CE-Cholesterilester C15: 0 0.7417 to 1.71E-07 SM_Sphingomyelin (d17: 1, C23: 0) 0.7475 to 3.11E-06 Cholesterilester C18: 1 0.7761 to 3.96E-06 CE_Colesterilester C14: 1 0.7944 up to 0.020884 SM_Sphingomyelin (d16: 1, C24: 0) 0.8038 up to 0.000724 Testosterone 0.8043 up to 0.000486 Cryptoxanthin 0.8112 up to 0.022505 Tricosanoic acid (C23: 0) 0, 8181 to 3.34E-06

1-Hydroxy-2-amino- (cis, trans) -3.5- octadecadiene (* 1) 0.825 to 4.63E-05 SM_Sphingomyelin (d16: 1, C23: 0) 0.8316 to 0.000209 CE_Colesterilester C20: 5 0.8319 up to 0.033306 SM_Sphingomyelin (d17: 1, C24: 0) 0.8367 up to 2.64E-05 Uridine 0.8412 up to 0.001758 CE_Colesterilester C14: 0 0.842 up to 0.000177 CER_Ceramide (d17: 1, C23: 0) 0.846 to 0.006496 CE_Colesterilester C22: 6 0.8469 to 0.00898 Lignoceric acid (C24: 0) 0.8494 to 0.000266 SM_Sphingomyelin (d17: 1, C22: 0) 0.8503 to 7, 29E-05 Lysophosphatidylcholine (C17: 0) 0.8506 to 0.000088 SM_Sphingomyelin (d18: 1, C14: 0) 0.8509 to 6.05E-06 CER_Ceramide (d18: 1, C14: 0) 0.8555 to 0 , 0011 Sphingosine-1-phosphate (d17: 1) 0.8559 to 0.000609 SM_Sphingomyelin (d18: 2, C23: 0) 0.8569 to 1.14E-05 erythro-C16-Sphingosine 0.8571 to 0.004699 (continuation)

Metabolite regulation ratio p-value SM_Sphingomyelin (d18: 1, C22: 0) 0.8573 to 0.003078 CE_Collesterilester C22: 4 0.862 to 0.00321 CE_Colesterilester C22: 5 0.8626 to 0.000744

EC Colesterilester C18: 3 0.8635 to 0.029677 CER Ceramide (d18: 2, C14: 0) 0.8636 to 0.006569 CER_Ceramide (d17: 1, C24: 0) 0.868 to 0.012557 SM_Sphingomyelin (d18: 1 , C23: 0) 0.8682 to 1.14E-06 CE_Colesterilester C16: 2 0.8705 to 0.018289

5-O-Methylphingosine (* 1) 0.8721 to 0.001713 SM_Sphingomyelin (d17: 1, C16: 0) 0.8749 to 0.000247

3-O-Methylphingosine (* 1) 0.8767 to 0.003292

SM-Sphingomyelin (d18: 2, C24: 0) 0.8786 to 0.000404 Cholesterilester C20: 4 0.8789 to 0.010238 SM_Sphingomyelin (d18: 2, C23: 1) 0.8793 to 0.001284 Behenic acid ( C22: 0) 0.8794 to 0.000548 SM_Sphingomyelin (d16: 1, C22: 0) 0.881 to 0.006635 CE_Colesterilester C20: 1 0.8816 to 0.042835 Isopalmitic acid (C16: 0) 0.8832 to 0, 042059 Cholesta-2,4-diene 0.8859 up to 0.003899 CE_Colesterilester C18: 0.888 up to 0.048452 SM_Sphingomyelin (d16: 1, C21: 0) 0.8875 up to 0.019946

SM Sphingomyelin (d18: 1, C23: 1) 0.8877 to 0.000701 CER_Ceramide (d17: 1, C16: 0) 0.8889 to 0.010878 Sphingadienin-1-phosphate (d18: 2) 0.89 to 0 , 002201 SM_Sphingomyelin (d18: 2, C24: 2) 0.8904 to 0.001593 Linoleic acid (C18: cis [9.12] 2) 0.8922 to 0.016504 threo-Sphingosine (* 1) 0.893 to 0, 001104 SM_Sphingomyelin (d18: 2, C22: 0) 0.8939 to 0.005627 erythro-Sphingosine (* 1) 0.8942 to 0.002435 SM_Sphingomyelin (d17: 1, C20: 0) 0.8946 to 0.005248 Cholesta -2,4,6-triene 0.8959 to 0.003304

SM-Sphingomyelin (d18: 1, C24: 0) 0.9 to 0.001163 Sphingosine-1-phosphate (d18: 1) 0.9 to 0.009618

EC-Cholesterilester C20: 2 0.9003 to 0.01281 Lysophosphatidylcholine (C18: 0) 0.9015 to 0.005621 SM_Sphingomyelin (d18: 1, C24: 2) 0.902 to 0.00413

SM-Sphingomyelin (d18: 2, C21: 0) 0.9059 to 0.011496 Cholesterol, total 0.906 to 0.000403 SM_Sphingomyelin (d17: 1, C24: 1) 0.9066 to 0.003313 SM_Sphingomyelin (d18: 1, C21: 0) 0.9083 to 0.002526 SM_Sphingomyelin (d18: 2, C14: 0) 0.9137 to 0.036132 Lysophosphatidylcholine (C18: 2) 0.9161 to 0.029008 SM_Sphingomyelin (d18: 1, C16: 0 ) 0.9172 to 0.001946 SM_Sphingomyelin (d17: 1, C18: 0) 0.9186 to 0.032136

EC Colesterilester C16: 0 0.9193 to 0.014685 Phosphatidylcholine (C16: 1, C18: 2) 0.9291 to 0.029842

SM Sphingomyelin (d18: 0, C16: 0) 0.9308 to 0.010735 Phosphatidylcholine (C16: 0, C20: 4) 0.9933 to 0.038201

Table 2A.1: Metabolites that are significantly increased in plasma (p value <0.05) among asymptomatic CH (DCMP) patients with NYHA I and controls

METABOLITE regulation ratio p-value Betaine 1,345 to 0,047

1-Methylhistidine 1,278 to 0,024

N, N-Dimethylglycine 1,264 to 0.015

TAG (C16: 0, C18: 1, C18: 2) 1,2557 to 0.048383 Proline 1,1309 to 0.038022 Table 2A.2: Metabolites that are significantly decreased in plasma (p value <0.05) among asymptomatic CHF (DCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Biliverdin 0.562 to 0.027

Erythro-C16-Sphingosine 0.8592 to 0.029152

Threo-Sphingosine (* 1) 0.8906 to 0.013527

5-O-Methylphingosine (* 1) 0.8943 to 0.047886

CE-Cholesterilester C16: 0 0.9077 up to 0.043609

Table 3A.1: Metabolites that are significantly increased in plasma (p value <0.05) among asymptomatic CHF patients (ICMP) with NYHA I and controls

METABOLITE regulation value p value

4-Hydroxy-3-methoxydendic acid 1,6392 up to 0,011879

TAG (C16: 0, C18: 1, C18: 2) 1,572 to 4.23E-05

TAG (C18: 1, C18: 2) 1.5059 to 2.41 E-05

TAG (C16: O, C16: 1) 1.4663 to 0.00067

TAG (C18: 2, C18: 2) 1.4275 to 0.000482

15-hydroxyeicosatetraenoic acid (C20: cis [5,8,11,13] 4) 1,3706 to 0.002652

TAG (C18: 2, C18: 3) 1.3664 to 0.020521

TAG (C16: 0, C18: 1, C18: 3) 1.3518 to 0.003772

8-hydroxyeicosatetraenoic acid (C20: [5] trans [9,11,14] 4) 1,3335 to 0.004867

(8-HETE)

14,15-Dihydroxyeicosatrienoic acid (C20: cis [5,8,11] 3) 1,332 to 0.000468

DAG (C18: 1, C18: 2) 1.3115 to 0.001439

Pyruvate 1.2874 to 0.005757

trans-4-Hydroxyproline 1,2253 up to 0,029368

SM_Sphingomyelin (d18: 0, C18: 0) 1,187 to 0.021168

Oleic acid (C18: cis [9] 1) 1.1837 to 0.035226

Alanine 1.1628 up to 0.001332

Proline 1.1575 to 0.013187

CER Ceramide (d18: 1, C18: 0) 1,1554 to 0,020581

Phosphatidylcholine (C18: 0, C20: 4) 1.0451 to 0.001529

Phosphatidylcholine (C16: 0, C18: 2) 1.0142 to 0.017665

Table 3A.2: Metabolites that are significantly decreased in plasma (p value <0.05) among asymptomatic CHF patients (ICMP) with NYHA I and controls

METABOLITE regulation value p value

CE_Clesterilester C12: 0 0.6027 up to 0.00105

CE_Colesterilester C20: 1 0.7369 up to 0.00017

CE_Colesterilester C18: 0 0.7737 to 0.00143

Sphingosine-1-phosphate (d17: 1) 0.803 to 0.00025

Erythro-C16-Sphingosine 0.8147 up to 0.00311

SM_Sphingomyelin (d18: 2, C23: 1) 0.8247 to 0.00017

5-O-Methylphingosine (* 1) 0.8249 to 0.00065

CE_Colesterilester C20: 2 0.8323 to 0.00076

Spingadienin-1-phosphate (d18: 2) 0.8333 to 0.00026

3-O-Methylphingosine (* 1) 0.8364 to 0.00203

Sphingosine-1-phosphate (d18: 1) 0.8409 up to 0.00118

Threo-Sphingosine (* 1) 0.8433 to 0.00014

SM_Sphingomyelin (d18: 2, C24: 2) 0.8439 to 0.00035

SM_Sphingomyelin (d18: 2, C14: 0) 0.8444 to 0.00223

SM-Sphingomyelin (d18: 1, C23: 1) 0.8516 to 0.00045

Erythro-Sphingosine (* 1) 0.8604 to 0.0016

SM_Sphingomyelin (d18: 1, C24: 2) 0.8659 to 0.00206

Sphinganine-1-phosphate (d18: 0) 0.8684 to 0.01946

Phytosphingosine, total 0.8783 to 0.01206

SM_Sphingomyelin (d18: 1, C16: 0) 0.8816 to 0.00039

SM-Sphingomyelin (d16: 1, C24: 1) 0.8836 to 0.01867

SM_Sphingomyelin (d16: 1, C16: 0) 0.8877 to 0.01607

CE_Colesterilester C16: 0 0.8917 to 0.01189

Lysophosphatidylethanolamine (C22: 5) 0.8927 to 0.02336

SM_Sphingomyelin (d18: 2, C21: 0) 0.8977 to 0.0347

FS_Colesterol, free 0.8988 to 0.02029

SM_Sphingomyelin (d18: 2, C24: 1) 0.9026 to 0.04993

(continuation)

METABOLITE regulation value p value

Galactose, lipid fraction 0.9077 to 0.02201

SM_Sphingomyelin (d18: 0, C16: 0) 0.9102 to 0.0106

SM Sphingomyelin (d18: 1, C21: 0) 0.9157 to 0.03569

Phosphatidylcholine (C16: 0, C20: 4) 0.9872 to 0.00257

Table 4A.1: Metabolites that are significantly increased in plasma (p value <0.05) among asymptomatic CHF (HCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Serotonin (5-HT) 2,798 to 2,02E-05

12-hydroxyeicosatetraenoic acid (C20: cis [5,8,10,14] 4) 1,9119 to 0.000315

Spingadienin (d18: 2) 1.5668 to 4.78E-05

Sphingosine (d16: 1) 1.5457 to 4.24E-06

TAG (C18: 1, C18: 2) 1,468 to 0.000122

TAG (C16: 0, C18: 1, C18: 2) 1.4301 to 0.001631

Lauric acid (C12: 0) 1.4093 to 0.035036

TAG (C16: 0, C16: 1) 1,3801 to 0.005457

15-hydroxyeicosatetraenoic acid (C20: cis [5,8,11,13] 4) 1.3749 to 0.002774

Pyruvate 1,3282 up to 0.002719

TAG (C18: 2, C18: 2) 1.3192 to 0.008317

indole-3-acetic acid 1.2619 to 0.032701

TAG (C16: 0, C18: 1, C18: 3) 1.2477 to 0.039006

Oleic acid (C18: cis [9] 1) 1,203 to 0,025681

DAG (C18: 1, C18: 2) 1.1925 to 0.044326

Ketoleucine 1.1858 up to 0.006086

Aspartate 1,1185 to 0,021727

Table 4A.2: Metabolites that are significantly decreased in plasma (p value <0.05) among asymptomatic CHF (HCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Hypoxanthine 0.7523 to 0.022159

Cholesterilester C20: 4 0,7791 up to 0.000275

Sphingadienin-1-phosphate (d18: 2) 0.8643 to 0.004555

Sphingosine-1-phosphate (d17: 1) 0.8706 to 0.024222

Sphingosine-1-phosphate (d18: 1) 0.885 to 0.026186

SM_Sphingomyelin (d18: 2, C21: 0) 0.8871 to 0.020913

SM_Sphingomyelin (d18: 2, C23: 1) 0.8935 to 0.029416

SM_Sphingomyelin (d18: 1, C21: 0) 0.894 to 0.008521

SM_Sphingomyelin (d18: 2, C24: 2) 0.9029 to 0.032679

SM_Sphingomyelin (d18: 1, C23: 1) 0.9039 to 0.028641

Glycine 0.91 to 0.034846

Serine 0.9157 to 0.036484

Table 5A.1: Metabolites that are significantly increased in plasma (p <0.05) in asymptomatic CHF patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Maltose 1.8612 up to 7.26E-09

Sucrose 1.6063 up to 0.000154

Fructose 1,585 to 2.94E-05

Sorbitol 1.5818 to 7.64E-05

TAG (C16: 0, C18: 1, C18: 2) 1.4267 up to 0.000062

Glutamate 1.4127 to 4.28E-06

Glycerol, lipid fraction 1.4013 to 0.000211

4-Hydroxy-3-methoxydendic acid 1.3955 to 0.028808

Lixosa 1,3804 up to 0,030667

TAG (C16: 0, C18: 2) 1,3694 to 7.76E-06

Norepinephrine (norepinephrine) 1.3635 to 5.28E-06

Normetanephrine 1.3632 to 0.000483

4-Hydroxy-3-methoxyphenyl glycol (HMPG) 1.3534 to 0.01075

TAG (C16: 0, C16: 1) 1,3497 to 0.000863

Palmitoleic acid (C16: cis [9] 1) 1.3325 to 0.000469

Pyruvate 1.3196 to 0.000205

(continuation)

METABOLITE regulation ratio p-value TAG (C18: 1, C18: 2) 1.3195 to 0.000365 12-hydroxyeicosatetraenoic acid (C20: cis [5,8,10,14] 4) 1,3113 to 0,048662 CER_Ceramide ( d18: 1, C18: 0) 1.2855 to 3.29E-07 Lauric acid (C12: 0) 1.2772 to 0.049643 8-hydroxyeicosatetraenoic acid (C20: [5] trans [9,11,14] 4 )

(8-HETE) 1,2757 to 0.0026 Glucuronic acid 1.2675 to 0.005141 TAG (C18: 2, C18: 2) 1.2547 to 0.005473 isocitrate 1.2531 to 6.62E-08 alpha- Ketoglutarate 1.2439 to 4.07E-06 SM_Sphingomyelin (d18: 0, C18: 0) 1.2357 to 0.000328 Sphingadienin (d18: 2) 1.2328 to 0.017646 Sphingosine (d18: 1) 1.2306 to 0.020774 Uric acid 1.2262 to 1.8E-07 Oleic acid (C18: cis [9] 1) 1.2224 to 0.001794 GAD (C16: 0, C18: 1, C18: 3) 1,221 to 0, 016573 Eritrol 1,2022 up to 7,11E-05 Cystine 1,2021 up to 0,000402 DAG (C18: 1, C18: 2) 1,1986 up to 0,00791 Ribonic acid 1,1951 up to 0.000337 15-hydroxyeicosatetraenoic acid ( C20: cis [5,8,11,13] 4) 1,1881 to 0.034394 trans-4-Hydroxyproline 1.1862 to 0.023224 Eicosenoic acid (C20: cis [11] 1) 1.1825 to 0, 007312 Taurine 1,1791 to 0.001355 3-Methoxytyrosine 1.1767 to 0.005763 Malate 1.1759 to 0.000956 Heptadecenoic acid (C17: cis [10] 1) 1.1753 to 0.006988 Sphinganine (d18: 0 ) 1.1618 to 0.044493 CER_Ceramide (d18: 1, C20: 0) 1.1613 to 0.00173 Pseudouridine 1.1542 to 1.12E-05 CER_Ceram! Da (d18: 2, C18: 0) 1.1532 to 0.008558 14,15-Dihydroxyeicosatrienoic acid (C20: cis [5.8 , 11] 3) 1.1514 to 0.029153 2-Hydroxybutyrate 1.1499 to 0.019381 Mannose 1,1398 to 0.00612 5-Hydroxy-3-indoleacetic acid (5-HIAA) 1,1393 to 0.021697 Glycerol-3-phosphate, polar fraction 1.1316 to 0.017351 Lactate 1.1211 to 0.04896 Glucose-1-phosphate 1.1131 to 0.0053 Cysteine 1.1094 to 0.000149 CER_Ceramide (d18: 1, C16 : 0) 1,1089 to 0.001362 Ornithine 1.1082 to 0.002282 CER_Ceramide (d18: 1, C24: 1) 1.107 to 0.00849 Pentoses 1.1047 to 0.018861 arachidonic acid (C20: cis [5, 8,11,14] 4) 1,103 up to 0,022057 Isoleucine 1,1018 up to 0,00346 CER_Ceramide (d18: 2, C20: 0) 1,0999 up to 0,048877 Sarcosine 1,0927 up to 0,000621 Alanine 1,0916 up to 0.016944 Tyrosine 1.0894 up to 0.012005 myo-Inositol 1.0844 up to 0.026308 Glycolate 1.0837 up to 0.022567 Glucose 1.0758 up to 0.003677 Phenylalanine 1.071 up to 0.007539 Fumarate 1.0647 to 0.0028 5-Oxoproline 1.0607 to 0.00611 Phosphatidylcholine (C18: 0, C20: 3) 1.0484 to 0.01459 Phosphatidylcholine (C18: 0, C20: 4) 1.0364 to 0 , 001209 Phosphatidylcholine (C18: 0, C18: 2) 1.0101 to 0.009469 Table 5A.2: Metabolites that are significantly decreased in plasma (p value <0.05) in asymptomatic CHF patients with NYHA II or NYHA III versus controls

METABOLITE regulation ratio p-value Dehydroepiandrosterone sulfate 0.6243 up to 0.000209 Hippuric acid 0.6488 up to 0.00112 12-Hydroxyheptadecatrienoic acid (C17: [5,8,10] 3) 0,6594 up to 0,033157 Beta-carotene 0.662 to 1.64E-06 SM_Sphingomyelin (d17: 1, C23: 0) 0.6696 to 1.9E-09 CE_Colesterilester C15: 0 0.6857 to 1.99E-10 Colesterilester C18: 2 0.6865 to 4.3E -11 SM_Sphingomyelin (d16: 1, C23: 0) 0.7125 up to 2.79E-10 CER_Ceramide (d17: 1, C23: 0) 0.723 up to 3.99E-07 CE_Colesterilester C12: 0 0.7277 up to 0.00855 SM_Sphingomyelin (d16: 1, C24: 0) 0.7279 to 4.04E-06 SM_Sphingomyelin (d17: 1, C24: 0) 0.7281 to 5.13E-12 CER_Ceramide (d17: 1, C24: 0) 0.7351 up to 1.95E-07 Tricosanoic acid (C23: 0) 0.7383 up to 1.89E-11 CER_Ceramide (d16: 1, C23: 0) 0.7499 up to 2.54E-05 CE_Colesterilester C20: 5 0.7517 up to 0 , 00137 CER_Ceramide (d16: 1, C24: 0) 0.7575 to 2.33E-05 1-Hydroxy-2-amino- (cis, trans) -3,5- octadecadiene (* 1) 0,7579 to 4, 01E-08 SM_Sphingomyelin (d17: 1, C22: 0) 0.771 3 to 3,4E-09 CE_Collesterilester C14: 0 0.7714 to 5.18E-08 erythro-C16-Sphingosine 0.7758 to 0.000013 CE_Clesterilester C14: 1 0.7761 to 0.013282 C18 cholesterol: 1 0.7788 up to 1.62E-05 Sphingosine-1-phosphate (d17: 1) 0.7804 up to 3.32E-07 Cryptoxanthine 0.7917 up to 0.013181 SM_Sphingomyelin (d16: 1, C22: 0) 0.7919 up to 0.000003 Lignoceric acid (C24: 0) 0.7957 to 8.41 E-07 SM_Sphingomyelin (d16: 1, C21: 0) 0.7979 to 3.69E-05 SM_Sphingomyelin (d16: 1, C22: 1) 0.8018 to 2.67E-05 CE_Collesterilester C22: 6 0.8028 to 0.000803 Lysophosphatidylcholine (C17: 0) 0.8053 to 3.98E-07 SM_Sphingomyelin (d18: 2, C23: 0) 0.8061 to 1E-08

Isopalmitic acid (C16: 0) 0.8088 to 0.000766 3,4-Dihydroxyphenylacetic acid (DOPAC) 0.8189 to 0.009748 CER_Ceramide (d18: 2, C14: 0) 0.819 to 0.000498 SM_Sphingomyelin (d17: 1 , C20: 0) 0.8221 to 4.36E-06 Uridine 0.8245 to 0.000683 SM_Sphingomyelin (d17: 1, C16: 0) 0.8275 to 1.13E-06 CER_Ceramide (d17: 1, C22: 0 ) 0.8333 to 0.001787 SM-Sphingomyelin (d18: 1, C14: 0) 0.8355 to 2.01E-06 SM_Sphingomyelin (d18: 1, C23: 0) 0.8387 to 4.96E-09 SM_Sphingomyelin ( d16: 1, C18: 1) 0.8413 to 0.000587 SM_Sphingomyelin (d18: 2, C24: 0) 0.8429 to 1.12E-05 Testosterone 0.8449 to 0.008317 SM_Sphingomyelin (d18: 2, C23: 1) 0.8467 to 0.000088 SM_Sphingomyelin (d17: 1, C24: 1) 0.8473 to 3.39E-06 Behenic acid (C22: 0) 0.8519 to 2.92E-05 CER_Ceramide (d18: 2, C23: 0) 0.8534 to 0.002371 CER_Ceramide (d18: 1, C14: 0) 0.8553 to 0.001421 CE_Colesterilester C16: 2 0.8556 to 0.009829 SM_Sphingomyelin (d16: 1, C20: 0) 0 , 8698 to 0.00363 SM_Sphingomyelin (d16: 1, C24: 1) 0.875 to 0, 002279 Docosahexanoic acid

(C22: cis [4,7,10,13,16,19] 6) 0.8813 to 0.047538 SM_Sphingomyelin (d18: 2, C14: 0) 0.8816 to 0.005791 Threonic acid 0.882 to 0.010602 CER_Ceramide (d16: 1, C22: 0) 0.8839 to 0.048345 SM_Sphingomyelin (d18: 1, C23: 1) 0.8846 to 0.000675 SM_Sphingomyelin (d18: 2, C24: 2) 0.885 to 0.001227 Lysophosphatidylcholine (C18: 2) 0.8857 to 0.00434 Linoleic acid (C18: cis [9.12] 2) 0.8858 to 0.015927

METABOLITE regulation value p value SM_Sphingomyelin (d18: 1, C24: 0) 0.8865 to 0.000308 SM_Sphingomyelin (d18: 2, C22: 0) 0.8867 to 0.004989

SM Sphingomyelin (d16: 1, C16: 0) 0.8915 to 0.00491

CER Ceramide (d17: 1, C24: 1) 0.882 to 0.024024 CER_Ceramide (d18: 2, C24: 0) 0.8922 to 0.018664 Colesta-2,4,6-triene 0.8931 to 0.00323 CER_Ceramide (d17: 1, C16: 0) 0.8938 to 0.017854

Sphingomyelin SM (d18: 1, C22: 0) 0.8983 to 0.043974 CER_Ceramide (d18: 1, C23: 0) 0.8984 to 0.02276 SM_Sphingomyelin (d17: 1, C18: 0) 0.8998 to 0 , 01183 SM_Sphingomyelin (d18: 2, C21: 0) 0.8998 up to 0.010662 SM_Sphingomyelin (d18: 2, C18: 1) 0.9005 up to 0.007649 Cholesta-2,4-diene 0.9016 up to 0.015946

5-O-Methylphingosine (* 1) 0.9018 to 0.020587 Glycine 0.9029 to 0.003028 Sphingadienin-1-phosphate (d18: 2) 0.9041 to 0.012127 CE_Colesterilester C22: 5 0.906 to 0.027417 Threo-Sphingosine (* 1) 0.9084 to 0.006841

3-O-Methylphingosine (* 1) 0.9096 to 0.038554 SM_Sphingomyelin (d18: 2, C20: 1) 0.9137 to 0.033919 Sphingosine-1-phosphate (d18: 1) 0.915 to 0.038706 Lysophosphatidylcholine ( C18: 0) 0.9161 to 0.022419 Erythro-Sphingosine (* 1) 0.9191 to 0.025385 Cholesterol, total 0.9215 to 0.004207 SM_Sphingomyelin (d18: 2, C22: 1) 0.922 to 0.030621 Phosphatidylcholine (C16: 0, C20: 5) 0.9296 to 0.007768 SM_Sphingomyelin (d18: 1, C21: 0) 0.9356 to 0.041029 SM_Sphingomyelin (d18: 1, C16: 0) 0.937 to 0.022708

SM Sphingomyelin (d18: 0, C16: 0) 0.9424 to 0.039238 Phosphatidylcholine (C18: 2, C20: 4) 0.9546 to 0.007472 Phosphatidylcholine (C16: 0, C20: 4) 0.9887 to 0 , 000723

Table 6A.1: Metabolites that are significantly increased in plasma (p <0.05) in asymptomatic DCMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation ratio p-value Guanosine monophosphate, cyclic (cGMP) 2,200 to 0,015762 Betaine 1,984 to 0,000539 Fructose 1,9027 to 7.2E-06 Maltose 1,8337 to 1.49E-06 Normetanephrine 1,7362 to 1 , 84E-07

4-Hydroxy-3-methoxydendic acid 1,7004 to 0,010391 Adrenaline (Epinephrine) 1,6556 to 0,000359 Norepinephrine (norepinephrine) 1,5953 to 2,78E-08

4-Hydroxy-3-methoxyphenyl glycol (HMPG) 1,5698 up to 0.002443

GAD (C16: 0, C18: 1, C18: 2) 1.5436 to 0.000146 Glutamate 1.5318 to 1.75E-06 Sucrose 1.5222 to 0.007055 Lixose 1.4918 to 0.04076 Kinurenine 1,461 to 0.006707

TAG (C16: 0, C18: 2) 1.4401 to 5.44E-05

1-Methylhistidine 1,426 to 0,011682

GAD (C18: 1, C18: 2) 1.4219 to 0.000435 Glucuronic acid 1.4131 to 0.002479

3-Methoxytyrosine 1.4075 to 1.39E-05 Sorbitol 1.404 to 0.015494 isocitrate 1.3897 to 5.22E-11

N, N-Dimethylglycine 1,378 to 0.009902 alpha-ketoglutarate 1,3456 to 2.67E-06

GAD (C16: 0, C16: 1) 1,3449 to 0,010755 Malate 1,3338 to 5,24E-06 Prostaglandin D2 1,3241 to 0,003317 Hill 1,302 to 0,01099 Glycerol, lipid fraction 1,298 to 0, 026168

(continuation)

METABOLITE regulation ratio p-value 8-hydroxyeicosatetraenoic acid (C20: [5] trans [9,11,14] 4) 1,2893 to 0.01092

(8-HETE)

TAG (C18: 2, C18: 2) 1,2887 to 0.015905

TAG (C16: 0, C18: 1, C18: 3) 1,279 to 0,022255

N, N-Dimethylarginine (ADMA) 1,275 to 0.003233 Ribonic acid 1,2754 to 7.31E-05 Sphingosine (d18: 1) 1.2619 to 0.04417 Carnitine 1,256 to 0.030164 CER_Ceramide (d18: 1, C18 : 0) 1.2549 to 0.000174 trans-4-Hydroxyproline 1.2455 to 0.015447 Eritrol 1.2236 to 0.000267 Uric acid 1.2131 to 6.69E-05 14,15-Dihydroxyeicosatrienoic acid (C20: cis [5,8,11] 3) 1,2078 to 0,017081 DAG (C18: 1, C18: 2) 1,2042 to 0,034391 13-hydroxyoctadecadienoic acid (13-HODE) (C18: cis [9] 1.1878 to 0.035187 trans [11] 2)

Creatine 1.1854 to 0.030902 5-Hydroxy-3-indoleacetic acid (5-HIAA) 1,1786 to 0.025837 Cystine 1.1522 to 0.026543 Ornithine 1,1476 to 0.001497 Pseudouridine 1,1458 to 0 , 000741 Homoserine 1.1422 to 0.021709 CER Ceramide (d18: 1, C20: 0) 1,1392 to 0.02812 CER_Ceramide (d18: 1, C16: 0) 1,1368 to 0.001467 Glycerol-3-phosphate , polar fraction 1,1367 to 0.047191 Proline 1,1351 to 0.026886 Pentoses 1,1339 to 0.017548 myo-Inositol 1,1268 to 0.006009 Tyrosine 1,114 to 0.010745 Citrate 1,1116 to 0.028918 Isoleucine 1.1112 to 0.0105 Hexadecanol 1.1103 to 0.046868

5-Oxoproline 1.1092 to 0.000298 CER_Ceramide (d18: 1, C24: 1) 1.1009 to 0.046187 Phenylalanine 1.0919 to 0.005826 Sarcosine 1.0863 to 0.009006 Fumarate 1.0795 to 0, 004964 Phosphatidylcholine (C18: 0, C18: 2) 1,011 to 0,033217

Table 6A.2: Metabolites that are significantly decreased in plasma (p value <0.05) in symptomatic MPDD patients with NYHA II or NYHA III versus controls

METABOLITE P-value regulation ratio dehydroepiandrosterone sulfate 0.5178 to 6.03E-05 SM_Sphingomyelin (d17: 1, C23: 0) 0.5651 to 5.24E-13 SM_Sphingomyelin (d16: 1, C24: 0) 0.603 to 4, 96E-10 CE_Colesterilester C20: 5 0.6079 up to 6.3E-06 Hippuric acid 0.6092 up to 0.004051 SM_Sphingomyelin (d16: 1, C23: 0) 0.6162 up to 4.31 E-14 CE_Colesterilester C15: 0 0 , 6334 to 3.38E-10 CE_Colesterilester C14: 1 0.6428 to 0.00125 SM_Sphingomyelin (d17: 1, C24: 0) 0.6433 to 5.96E-16 erythro-C16-Sphingosine 0.6456 to 2.33E -10 C18 Cholesterilester: 0.6644 to 1.01E-09 CER_Ceramide (d17: 1, C23: 0) 0.6651 to 2.96E-07

1-Hydroxy-2-amino- (cis, trans) -3.5- octadecadiene (* 1) 0.6659 to 9.04E-12 SM_Sphingomyelin (d16: 1, C22: 0) 0.6678 to 1.17E- 11 Eicosapentaenoic acid (C20: cis [5,8,11,14,17] 5) 0,6684 to 0,000223

EC Colesterilester C12: 0 0.6723 to 0.007976 CER Ceramide (d16: 1, C23: 0) 0.6728 to 3.18E-06 CER Ceramide (d17: 1, C24: 0) 0.6742 to 7.34E -08 CER_Ceramide (d16: 1, C24: 0) 0.6754 to 1.48E-06 SM_Sphingomyelin (d17: 1, C22: 0) 0.6766 to 6.68E-14 Tricosanoic acid (C23: 0) 0.6836 up to 5,19E-12 (continuation)

METABOLITE regulation value p docosahexanoic acid (C22: cis [4,7,10,13,16,19] 6) 0.688 to 9.55E-06 CE_Colesterilester C18: 4 0.6942 to 0.005135 SM_Sphingomyelin (d16: 1, C21: 0) 0.695 to 3.52E-08 SM_Sphingomyelin (d16: 1, C22: 1) 0.6898 up to 1.01E-08 Beta-carotene 0.7004 up to 0.000766 CE_Colesterilester C22: 6 0.7008 up to 1, 12E-05 Isopalmitic acid (C16: 0) 0.7008 to 2.62E-05 Lignoceric acid (C24: 0) 0.7092 to 1.79E-08 CE_Colesterilester C14: 0 0.7099 to 6.54E-08 SM_Sphingomyelin ( d17: 1, C20: 0) 0.7152 to 5.94E-11 SM Sphingomyelin (d16: 1, C20: 0) 0.7269 to 2.65E-08 CER_Ceramide (d18: 2, C14: 0) 0.7332 up to 5.89E-06 Uridine 0.741 up to 0.000021 CE_Collesterilester C16: 2 0.7418 up to 0.000216 SM_Sphingomyelin (d16: 1, C18: 1) 0.7505 up to 2.01E-06 Campesterol 0.7505 up to 0.009985 Sphingosine-1-phosphate (d17: 1) 0.7517 to 4.93E-06 5-Methylcytidine 0.752 to 0.045389 SM_Sphingomyelin (d18: 2, C23: 0) 0.7572 to 3.68E-10 Behenic acid (C22 : 0) 0.7627 to 8,15E-08 SM_Sphingomyelin (d17: 1, C16: 0) 0,7659 to 7,79E-09 SM_Sphingomyelin (d17: 1, C24: 1) 0,769 to 6,88E-10 SM_Sphingomyelin (d16: 1, C24: 1 ) 0.7699 to 4.73E-07 Docosapentaenoic acid (C22: cis [7,10,13,16,19] 5) 0.7716 to 0.001219 CE_C16 Cholesterilester: 0.773 to 0.002649 CE C18 Cholesterilester: 3 0.7735 to 0.005704 CER_Ceramide (d17: 1, C22: 0) 0.7783 to 0.00058 Lysophosphatidylcholine (C17: 0) 0.7786 to 2.26E-06 14-methylhexadecanoic acid 0.7813 to 0.005137 SM_Sphingomyelin (d17: 1, C18: 0) 0.7841 to 1.33E-06 Conjugated linoleic aco (C18: trans [9.11] 2) 0.7908 to 0.004506 SM Sphingomyelin (d18: 2, C24: 0 ) 0.7957 to 6.98E-07 METABOLIT regulation value p-value CER_Ceramide (d16: 1, C22: 0) 0.7958 to 0.003303 SM_Sphingomyelin (d18: 1, C14: 0) 0.7979 to 3.17E- 07 SM_Sphingomyelin (d18: 2, C23: 1) 0.7996 to 8.03E-06 SM_Sphingomyelin (d16: 1, C18: 0) 0.8032 to 0.00012 SM_Sphingomyelin (d18: 1, C23: 0) 0.8062 up to 5.71E-09 SM_Sphingomyelin (d18: 2, C22: 0) 0.8 13 to 4.34E-05 Lysophosphatidylcholine (C18: 2) 0.8147 to 0.000061 CER Ceramide (d18: 1, C14: 0) 0.8158 to 0.000794 CER_Ceramide (d18: 2, C24: 0) 0, 8159 to 0.001869 SM Sphingomyelin (d16: 1, C16: 0) 0.8163 to 2.85E-05 CER_Ceramide (d18: 2, C23: 0) 0.8165 to 0.001854 Cholesterilester C18: 1 0.8179 to 0.006827 CER_Ceramide (d16: 1, C21: 0) 0.818 to 0.023479 Nicotinamide 0.8202 to 0.031822 SM_Sphingomyelin (d18: 2, C14: 0) 0.8225 to 0.000287 Linoleic acid (C18: cis [ 9.12] 2) 0.8272 to 0.00319 CER_Ceramide (d17: 1, C20: 0) 0.8284 to 0.009011 Colesta-2,4,6-triene 0.8315 to 0.000317 Testosterone 0.8318 up to 0.02077 Cholesta-2,4-diene 0.8352 up to 0.001569 SM Sphingomyelin (d18: 1, C22: 0) 0.8387 up to 0.006899 CER Ceramide (d17: 1, C16: 0) 0.8396 up to 0.003059 CER Ceramide (d16: 1, C16: 0) 0.8416 up to 0.005905 CER_Ceramide (d16: 1, C24: 1) 0.842 up to 0.01852 CE Colesterilester C20: 1 0.8452 up to 0.043754 CER_Ceramide (d17: 1, C24: 1) 0.8508 to 0.010596 SM_Sphingomyelin (d18: 1, C24: 0) 0.8562 to 0.000159 SM_Sphingomyelin (d18: 1, C23: 1) 0.8573 to 0.000532 SM_Sphingomyelin (d18: 2, C21: 0) 0.8594 to 0.002341 CE_Clesterilester C22: 5 0.865 to 0.009324 (continuation)

METABOLITE regulation value p value Phosphatidylcholine (C16: 0, C20: 5) 0.8653 to 1.98E-05 SM_Sphingomyelin (d18: 2, C18: 1) 0.8667 to 0.003474 threo-Sphingosine (* 1) 0, 8684 to 0.002273 Cholesterol, total 0.8712 to 0.000266 CER_Ceramide (d18: 1, C23: 0) 0.8724 to 0.020108

3-O-Methylphingosine (* 1) 0.8764 to 0.01855

5-O-Methylphingosine (* 1) 0.8797 to 0.018763 SM_Sphingomyelin (d18: 2, C20: 1) 0.8865 to 0.02317 erythro-Sphingosine (* 1) 0.8875 to 0.009672 Lysophosphatidylcholine (C18 : 0) 0.8882 to 0.013338 Ketoleucine 0.8887 to 0.046578

CER Ceramide (d18: 1, C24: 0) 0.8898 to 0.046562 Phosphatidylcholine (C18: 0, C22: 6) 0.8903 to 0.014505 CE_Colesterilester C16: 0 0.8939 to 0.017463 SM_Sphingomyelin (d18: 2, C20: 0) 0.8975 to 0.009035 Eicosanoic acid (C20: 0) 0.8984 to 0.019061 SM_Sphingomyelin (d18: 2, C22: 1) 0.8993 to 0.021648 Phytosphingosine, total 0.8993 up to 0.047255 SM_Sphingomyelin (d18: 2, C24: 2) 0.9014 up to 0.023444 Glycine 0.9037 up to 0.01812 Phosphatidylcholine (C18: 2, C20: 4) 0.9097 up to 0.00001 SM_Sphingomyelin (d18: 1, C21: 0) 0.9129 to 0.025161 Phosphatidylcholine No. 02 0.916 to 0.005922 Phosphatidylcholine (C16: 0, C20: 4) 0.9883 to 0.007338

Table 7A.1: Metabolites that are significantly increased in plasma (p value <0.05) in symptomatic ICMP patients with NYHA II or NYHA III versus controls

METABOLITE P-value regulation Sorbitol 1,8777 up to 0.000022 Maltose 1,709 up to 4.48E-05 Sucrose 1,702 up to 0.001181 4-Hydroxy-3-methoxydendic acid 1.5815 up to 0.032145 Glutamate 1.5798 up to 1.04E -06 Glycerol, lipid fraction 1,5322 to 0.000449 Fructose 1,5002 to 0.005719 Trans-ferulic acid 1,493 to 0.040879

TAG (C16: 0, C18: 1, C18: 2) 1.4701 up to 0.001033

O-Acetylcarnitine 1,446 to 0,013327 Palmitoleic acid (C16: cis [9] 1) 1,4396 to 0.000678

TAG (C16: 0, C18: 2) 1.4305 to 0.000118

GAD (C16: 0, C16: 1) 1,4269 to 0.003031 Pyruvate 1.4141 to 0.000413 Kinurenina 1,378 to 0.019997

1-Methylhistidine 1,373 to 0,022886 CER_Ceramide (d18: 1, C18: 0) 1,3694 to 8,88E-07 Carnitine 1,361 to 0,003736

SM Sphingomyelin (d18: 0, C18: 0) 1.3399 to 0.000126

TAG (C18: 2, C18: 3) 1,3271 to 0.049014

TAG (C18: 1, C18: 2) 1,327 to 0.005858 8-hydroxyeicosatetraenoic acid (C20: trans [5]

cis [9,11,14] 4) (8-HETE) 1,3143 to 0,010779 alpha-ketoglutarate 1,2985 to 5.62E-05 Oleic acid (C18: cis [9] 1) 1.2825 to 0 , 003745 Glucuronic acid 1,2774 to 0.036861 Eritrol 1.2766 to 2.77E-05

1-Methyladenosine 1,276 to 0,022777

DAG (C18: 1, C18: 2) 1,2758 to 0.007223 trans-4-Hydroxyproline 1,2643 to 0.013626 Uric acid 1,2603 to 5.76E-06 Normetanephrine 1.2513 to 0.039533

TAG (C18: 2, C18: 2) 1.2426 to 0.04473 isocitrate 1.2368 to 3.64E-05

(continuation)

METABOLITE regulation value p value CER_Ceramide (d18: 2, C18: 0) 1.2364 to 0.002679

2-Hydroxybutyrate 1,2229 to 0,010083 Cystine 1,2207 to 0.002991 Eicosenoic acid (C20: cis [11] 1) 1.2187 to 0.018113 CER_Ceramide (d18: 1, C20: 0) 1.2179 to 0.001609 gamma-Tocopherol 1.2177 to 0.045354 Mannose 1.2135 to 0.002405 Glucose-1-phosphate 1.2097 to 0.000187 Heptadecenoic acid (C17: cis [10] 1) 1.1971 to 0, 028966 14,15-Dihydroxyeicosatrienoic acid (C20: cis [5,8,11] 3) 1,1963 to 0,030698 Pseudouridine 1,1919 to 3,63E-05 Proline 1,1738 to 0,007696 Glycerol-3-phosphate , polar fraction 1,1706 to 0,020102 5-Hydroxy-3-indoleacetic acid (5-HIAA) 1,1653 to 0.043817 CER_Ceramide (d16: 1, C18: 0) 1,1651 to 0.048232 Glucosamine 1, 1606 to 0.049119 Alanine 1.1597 to 0.001954 Cysteine 1.1568 to 4.12E-05 Taurine 1.1497 to 0.031287 arachidonic acid (C20: cis [5,8,11,14] 4) 1, 1473 to 0.018388 Glucose 1,1362 to 0.00013 Isoleucine 1.1316 to 0.004264 Pentoses 1,1294 to 0.028117 Glycolate 1,1265 to 0.00978 myo-Inositol 1,1261 to 0, 009118 Sarcosine 1.1236 to 0.000585 Ornithine 1.1225 to 0.009489 Lysophosphatidylcholine (C20: 4) 1,1152 to 0.020675 Phenylalanine 1.0803 to 0.020618

5-Oxoproline 1.0778 up to 0.010757 Fumarate 1.0616 up to 0.032792 Phosphatidylcholine (C18: 0, C20: 4) 1.0584 up to 6.77E-05 Phosphatidylcholine (C18: 0, C20: 3) 1.0514 up to 0.048241 Phosphatidylcholine (C18: 0, C18: 2) 1,019 up to 0.00039 Phosphatidylcholine (C16: 0, C18: 2) 1.0137 up to 0.024577

Table 7A.2: Metabolites that are significantly decreased in plasma (p value <0.05) in symptomatic ICPM patients with NYHA II or NYHA III versus controls

METABOLITE regulation ratio p-value 12-Hydroxyheptadecatrienoic acid (C17: [5,8,10] 3) 0,4555 to 0.006291 Dehydroepiandrosterone sulfate 0.5619 to 0.000621

Beta-carotene 0.5768 to 9.19E-07 Cholesterilester C18: 2 0.6037 to 1.15E-12 SM_Sphingomyelin (d17: 1, C23: 0) 0.6205 to 5.19E-09 Hippuric acid 0.6511 to 0.015509 CE_Colesterilester C15: 0 0.6649 to 6.74E-08 SM_Sphingomyelin (d16: 1, C24: 0) 0.6716 to 2.24E-06 CE_Colesterilester C14: 1 0.6889 to 0.008073 SM_Sphingomyelin (d16: 1, C23: 0) 0.689 to 1.55E-08 CE_Colesterilester C12: 0 0.7002 to 0.022911 SM_Sphingomyelin (d17: 1, C24: 0) 0.7108 to 1.01E-09 Prostaglandin E2 0.7138 to 0 , 023861

1-Hydroxy-2-amino- (cis, trans) -3.5- octadecadiene (* 1) 0.7158 to 5.79E-08 Tricosanoic acid (C23: 0) 0.7173 to 6.13E-09 CE_Colesterilester C14 : 0 0.7286 to 1.13E-06 Cholesterilester C18: 1 0.73 to 4.49E-05 Lignoceric acid (C24: 0) 0.7446 to 2.27E-06 3,4-Dihydroxyphenylacetic acid (DOPAC) 0,752 up to 0.005707

EC Colesterilester C20: 1 0.7534 to 0.001053 CER_Ceramide (d17: 1, C23: 0) 0.7534 to 0.000596 erythro-C16-Sphingosine 0.7558 to 7.99E-05 SM_Sphingomyelin (d18: 2, C23 : 0) 0,7579 up to 2,32E-09

METABOLITE P-value regulation ratio SM_Sphingomyelin (d17: 1, C22: 0) 0.7632 to 4.02E-07 Sphingosine-1-phosphate (d17: 1) 0.7648 to 2.96E-05 CER_Ceramide (d17: 1, C24 : 0) 0.7695 to 0.000543 SM_Sphingomyelin (d18: 1, C14: 0) 0.7742 to 3.63E-08 Isopalmitic acid (C16: 0) 0.7773 to 0.003568 Cryptoxanthin 0.7788 to 0, 04299 SM_Sphingomyelin (d16: 1, C22: 0) 0.781 to 4.95E-05 SM_Sphingomyelin (d16: 1, C22: 1) 0.7812 to 0.000138 SM Sphingomyelin (d18: 2, C23: 1) 0.7836 to 3.56E-06 CER_Ceramide (d16: 1, C23: 0) 0.7865 to 0.006482 SM_Sphingomyelin (d17: 1, C16: 0) 0.7867 to 6.13E-07 SM_Sphingomyelin (d16: 1, C21: 0 ) 0.788 to 0.000475 SM Sphingomyelin (d18: 2, C24: 0) 0.7935 to 1.63E-06 CER Ceramide (d16: 1, C24: 0) 0.7947 to 0.006495 CER_Ceramide (d18: 2, C14: 0) 0.7961 to 0.001374 SM_Sphingomyelin (d17: 1, C24: 1) 0.799 to 3.84E-07 SM_Sphingomyelin (d18: 2, C24: 2) 0.7997 to 4.37E-06 Lysophosphatidylcholine (C17 : 0) 0.8009 to 5.84E-05 Linoleic acid (C18: cis [9,12] 2) 0.8 068 to 0.001222 CE_Colesterilester C16: 2 0.8077 to 0.009797 SM_Sphingomyelin (d18: 2, C14: 0) 0.8104 to 0.0002 SM_Sphingomyelin (d16: 1, C24: 1) 0.8123 to 0.000117 Threo-Sphingosine (* 1) 0.8166 to 2.39E-05 Behenic acid (C22: 0) 0.8205 to 0.00012 Cholesta-2,4,6-triene 0.8254 to 0.000278 SM_Sphingomyelin (d18: 1, C23: 0) 0.8258 to 6.58E-07 CE_Collesterilester C22: 6 0.8298 to 0.026263 SM_Sphingomyelin (d18: 2, C22: 0) 0.8298 to 0.000423 CE_Colesterilester C18: 0 0.8327 up to 0.032337 Threonic acid 0.834 up to 0.004723 5-O-Methylphingosine (* 1) 0.8356 up to 0.001748 CER_Ceramide (d18: 1, C14: 0) 0.8361 up to 0.004816 CE_Colesterilester C20: 2 0, 8409 to 0.001777 SM_Sphingomyelin (d18: 1, C16: 0) 0.8458 to 4.35E-06 Lysophosphatidylcholine (C18: 2) 0.8483 to 0.002028 SM_Sphingomyelin (d18: 1, C22: 0) 0.8483 up to 0.015892 Cholesta-2,4-diene 0.8486 up to 0.00508 Phytosphingosine, total 0.8489 up to 0.003058 SM_Sphingomyelin (d16: 1, C18: 1) 0.8489 up to 0.008822 3-O-Metilesfi ingosin (* 1) 0.8492 to 0.005506 SM_Sphingomyelin (d17: 1, C20: 0) 0.8503 to 0.001928 SM_Sphingomyelin (d16: 1, C16: 0) 0.8511 to 0.001427 SM Sphingomyelin (d18 : 1, C23: 1) 0.8544 to 0.000731 CER_Ceramide (d18: 2, C23: 0) 0.8611 to 0.028028 SM_Sphingomyelin (d18: 1, C24: 2) 0.8616 to 0.001756 SM_Sphingomyelin ( d18: 2, C21: 0) 0.8627 to 0.004634 SM Sphingomyelin (d18: 1, C24: 0) 0.8642 to 0.000691 CER_Ceramide (d18: 2, C24: 2) 0.8681 to 0.046836 Erythro-Sphingosine (* 1) 0.8699 to 0.004016 SM Sphingomyelin (d18: 2, C24: 1) 0.8714 to 0.00985 CER_Ceramide (d18: 2, C24: 0) 0.8718 to 0.040793 Sphingadienin -1-phosphate (d18: 2) 0.8727 to 0.010293 Cholesterol, total 0.874 to 0.000537 Sphingosine-1-phosphate (d18: 1) 0.8751 to 0.018886 SM_Sphingomyelin (d18: 2, C22: 1 ) 0.8792 to 0.007979 Glycine 0.8875 to 0.007978 Galactose, lipid fraction 0.8883 to 0.00878 SM_Sphingomyelin (d18: 2, C18: 1) 0.895 to 0.030212 CE_Collesterilester C16: 0.9019 0,033608 SM Esfingomi Elina (d18: 0, C16: 0) 0.9085 to 0.010531 Phosphatidylcholine (C16: 0, C20: 4) 0.9771 to 4.96E-07 Table 8A.1: Metabolites that are significantly increased in plasma (p value <0.05) in symptomatic HCMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation ratio p-value Maltose 1.9048 up to 4.63E-06 Sucrose 1.6319 up to 0.005063 12-hydroxyeicosatetraenoic acid (C20: cis [5,8,10,14] 4) 1,4659 up to 0,045714 Sorbitol 1,449 to 0,018141 Sphingadienin (d18: 2) 1,4046 to 0.003857 Glycerol, lipid fraction 1.4021 to 0.005953 Lauric acid (C12: 0) 1.4008 to 0.046554 Fructose 1.3913 to 0, 024517 Pyruvate 1.3862 to 0.001018 Palmitoleic acid (C16: cis [9] 1) 1,3808 to 0.004711 Sphingosine (d16: 1) 1.3506 to 0.002776

SM Sphingomyelin (d18: 0, C18: 0) 1.3198 to 0.00064

TAG (C16: 0, C16: 1) 1,2688 to 0.04947

Oleic acid (C18: cis [9] 1) 1,265 to 0,006908 Norepinephrine (norepinephrine) 1,2552 to 0,01379

Heptadecenoic acid (C17: cis [10] 1) 1.2412 to 0.009838 Taurine 1.2387 to 0.002057 Cystine 1.2339 to 0.003384 Palmitic acid (C16: 0) 1.2333 to 0.008969

GAD (C16: 0, C18: 2) 1.2318 to 0.025907 CER_Ceramide (d18: 1, C18: 0) 1.2204 to 0.003156 Uric acid 1.2023 to 0.000657 Eicosenoic acid (C20: cis [ 11] 1) 1,2003 to 0,031408

N, N-Dimethylaginine (ADMA) 1,193 to 0.034953

2-Hydroxybutyrate 1.1838 up to 0.042946 Stearic acid (C18: 0) 1.1822 up to 0.010717 CER_Ceramide (d18: 2, C18: 0) 1.1778 up to 0.029595 arachidonic acid (C20: cis [5, 8,11,14] 4) 1,175 to 0.006487 SM_Sphingomyelin (d18: 1, C24: 1) 1,1695 to 0.02742 erythro-Dihydrosphingosine 1,1688 to 0.028152 Glycerolphosphate, lipid fraction 1.1537 to 0, 044528 Ribonic acid 1.1534 to 0.036068 --dihomo-gamma-linoleic acid (C20: cis [8,11,14] 3) 1,1508 up to 0,04874 isocitrate 1,1491 up to 0,010786 Eritrol 1,1465 up to 0.026373 Cysteine 1.1423 up to 0.000431

Urea 1,1413 to 0,045112 CER_Ceramide (d18: 1, C24: 1) 1,1373 to 0.017432 Nerve acid (C24: cis [15] 1) 1,1302 to 0.023448 Arginine 1,1292 to 0, 03428 CER_Ceramide (d18: 1, C16: 0) 1,1208 to 0.011296 SM_Sphingomyelin (d18: 1, C18: 0) 1.1186 to 0.022073 SM_Sphingomyelin (d18: 2, C18: 0) 1,117 to 0.033572 Glucose-1-phosphate 1,1138 to 0.035621 Pseudouridine 1.1109 to 0.019312 SM_Sphingomyelin (d18: 2, C16: 0) 1.0871 to 0.036787 Glucose 1.0724 to 0.03724 Phosphatidylcholine (C18: 0 , C20: 3) 1.0644 to 0.021365 Phosphatidylcholine (C18: 0, C20: 4) 1.0411 to 0.007723

Table 8A.2: Metabolites that are significantly decreased in plasma (p value <0.05) in symptomatic HCPM patients with NYHA II or NYHA III versus controls

METABOLITE regulation ratio p-value Biliverdine 0.460 to 0.024536 12-Hydroxyheptadecatrienoic acid (C17: [5,8,10] 3) 0,5252 to 0,023105

Hippuric acid 0.6822 to 0.033329 Hypoxanthine 0.7217 to 0.012505

Beta-carotene 0.7326 up to 0.008399 Cryptoxanthine 0.7535 up to 0.031981 Cholesterilester C18: 2 0.7662 up to 0.000292

(continuation)

METABOLITE regulation value p Colesterilester C18: 1 0.79 to 0.002441 CER_Ceramide (d17: 1, C23: 0) 0.7963 to 0.00942 3,4-Dihydroxyphenylacetic acid (DOPAC) 0.8077 to 0.040606 CER_Ceramide (d17: 1, C24: 0) 0.8146 to 0.010918 Testosterone 0.8209 to 0.026895 CE_Collesterilester C15: 0 0.8273 to 0.016722 Sphingosine-1-phosphate (d17: 1) 0.8306 to 0 , 0041 SM_Sphingomyelin (d16: 1, C23: 0) 0.8454 to 0.014763 SM_Sphingomyelin (d17: 1, C24: 0) 0.8479 to 0.004647 CE_Colesterilester C14: 0 0.8591 to 0.019496 Lysophosphatidylcholine (C17 : 0) 0.8651 to 0.013217 Tricosanoic acid (C23: 0) 0.8765 to 0.027041 Sphingosine-1-phosphate (d18: 1) 0.8876 to 0.038364 Sphingadienin-1-phosphate (d18: 2 ) 0.8942 to 0.037477 SM_Sphingomyelin (d18: 2, C23: 0) 0.9093 to 0.049103 SM_Sphingomyelin (d18: 1, C23: 0) 0.919 to 0.03676

Table 11A.1: Metabolites showing progressive increase of controls on NYHA I to NYHA III in plasma of ICMP patients and controls

ratio (front control) regulation (front control) p-value (front control) METABOLITE NYHA I NYHA II NYHA III NYHA I NYHA II NYHA III NYHA I NYHA II NYHA III

Maltose 1.4331 1.8195 1.6064 up to 0.005 0.0002 0.0044 Glycerol, fraction

lipid 1,4351 1.4437 1.6239 up to 0.0015 0.012 0.0013 Pyruvate 1.2874 1.3944 1.4398 up to 0.0058 0.0055 0.0031 CER Ceramide

(d18: 1, C18: 0) 1,1554 1.2824 1.4672 up to 0.0206 0.0016 3E-06 SM Sphingomyelin

(d18: 0, C18: 0) 1,187 1,2096 1,4991 up to 0.0212 0.0423 3E-05 Oleic acid

(C18: cis [9] 1) 1.1837 1,166 1.4134 up to 0.0352 0.1374 0.0012 alpha-ketoglutarate 1,1326 1.2907 1.3222 up to 0.0383 0.0011 0 , 0005 Uric acid 1,1327 1,2388 1,2905 up to 0.0119 0.0007 9E-05 Proline 1.1575 1.0623 1.2973 up to 0.0132 0.4125 0.0007

Table 13A.1: Metabolites showing a significantly positive correlation (p <0.05) with plasma LVEF of CHF patients and controls

METABOLITE estimate p value correlation

Cholesterilester C18: 2 1,193968 positive 1,09E-11

Dehydroepiandrosterone Sulfate 1,187005 positive 0.003632

SM Sphingomyelin (d16: 1, C24: 0) 1.181787 positive 2.43E-07

SM Sphingomyelin (d17: 1, C23: 0) 1.179928 positive 2.12E-07

EC Cholesterilester C12: 0 1,166743 positive 0.00637

EC Cholesterilester C14: 1 1,164136 positive 0.001643

CE Cholesterilester C15: 0 1.1619 positive 1.57E-07

Beta-carotene 1,150177 positive 0.000254 SM_Sphingomyelin (d17: 1, C24: 0) 1,150079 positive 2.85E-11

Table 13A.2: Metabolites showing a significantly negative correlation (p <0.05) with plasma LVEF of CHF patients and controls

METABOLITE estimate p value correlation

4-Hydroxy-3-methoxydendic acid 0.771671 negative 0.000123

Sorbitol 0.772946 negative 1.03E-06

TAG (C16: 0, C18: 1, C18: 2) 0.802784 negative 2.74E-08

Maltose 0.817057 negative 1.94E-05

4-Hydroxy-3-methoxyphenylglycol 0.82735 negative 0.00037

(HMPG)

Norepinephrine (norepinephrine) 0.827734 negative 2.6E-09

TAG (C16: 0, C18: 2) 0.835743 negative 1.7E-08

Glutamate 0.83644 negative 3.99E-08

Glycerol, lipid fraction 0.837473 negative 7.42E-06 Normetanephrine 0.840455 negative 3.87E-06

TAG (C18: 1, C18: 2) 0.843536 negative 3.94E-06

GAD (C18: 2, C18: 2) 0.86407 negative 0.000258

TAG (C16: 0, C18: 1, C18: 3) 0.865611 negative 0.000278

DAG (C18: 1, C18: 2) 0.866014 negative 1.82E-06

Footnote to tables 1-13A.1 and 1--13A.2:

(* 1): free and sphingolipids

Table 1B.1: Metabolites that are significantly increased in plasma (p value <0.05) among all asymptomatic CHF patients with NYHA I and controls

METABOLITE regulation value p value

Salicylic acid 5.0668 to 0.000447

Furoylglycine 2,5783 up to 2.57E-05

Trans-ferulic acid 1.9521 to 0.000086

Pyrogallol 1,5992 up to 0,011331

Sucrose 1.5739 to 3.98E-05

Sorbitol 1.5128 up to 7.48E-08

Fructose 1.5122 up to 0.000227

1,3,7-trimethyluric acid 1,4888 up to 0.001375

4-hydroxyhipuric acid 1,4339 up to 0.000498

Glycerol-3-phosphate, polar fraction 1,414 to 6.07E-05

Lixosa 1.4088 to 1.39E-06

Arabinose 1.4017 to 1.53E-06

Cyclic Guanosine Monophosphate

(CGMP) 1.3737 to 1.32E-05

Glucuronic acid 1.3567 to 0.000293

Pentoses 1.3428 to 0.00038

N-Phenylacetylglycine 1,3396 to 0.032799

Threonic acid 1,3244 to 0.002694

Glucose-1-phosphate 1,3008 up to 0.00076

Glycerate 1,267 to 0,049406

Choline phosphate 1,2606 to 0,019771

Xylitol 1,2589 up to 0.001756

Fumarate 1,2365 to 0.000182

Normetanephrine 1,1886 to 0.00229

Ribonic acid 1.1874 to 0.000856

Citramalate 1.1843 up to 0.035691

(continuation)

METABOLITE regulation value p value

2-Deoxyribose 1,1786 to 0.028903

Arabitol 1,1781 up to 0.001567

cis-Aconite 1,1759 to 0.001545

Malate 1,1695 to 0,012958

Pyruvate 1,1672 to 0.006029

Norepinephrine (norepinephrine) 1.1629 to 0.022677

2-O-Methylascorbic acid 1.1542 up to 0.001271

Pentose acids 1,146 to 0,026758

Galactitol 1.1414 to 0.006146

1,1347 isocitrate up to 0.008337

Eritrol 1,1333 up to 0,004628

Allantoin 1,1324 to 0,03438

Xylulose 1.1112 up to 0.00495

Erythronic acid 1.1061 to 0.003409

Ribosa 1.0959 to 0.006876

Aspartate 1.0841 up to 0.040902

Cysteine 1.0694 to 0.045369

cis-4,5-Dihydroxy-1,2-dithian 1,0245 to 0,040415

Table 1B.2: Metabolites that are significantly decreased in urine (p value <0.05) among all asymptomatic CHF patients with NYHA I and controls

Metabolite ratio regulation value p

Androstenedione 0.4623 to 0.00001

Indole-3-lactic acid 0.5039 to 0.000132

Hippuric acid 0.5548 to 0.001727

7-Methyluric Acid 0.5934 up to 0.003567

7-Methylxanthine 0.6232 up to 0.001906

Carnitine 0.6609 up to 0.003232

Pantothenic Acid 0.6897 to 7.86E-05

O-Acetylcarnitine 0.7029 to 0.036364

1-Methyluric acid 0.7093 to 0.011982

Histidine 0.7384 up to 0.006349

beta-Aminoisobutyrate 0.744 to 0.039632

21-Hydroxyprogesterone (11-0.7652 to 0.022401 Deoxycorticoid ester)

Uracil 0.7663 up to 0.000037

Histamine 0.79 to 0.004107

Glycine 0.8028 up to 0.013839

Testosterone 0.8064 up to 0.025453

3,4-Dihydroxyphenylalanine (DOPA) 0.8275 to 0.019161

gamma-carboxyglutamate 0.833 to 0.00095

2-Methylserine 0.8469 to 0.006677

7-Methylguanosine 0.852 to 0.000468

N-Acetylhistidine 0.8539 to 0.003483

3-O-Methyldopamine 0.8546 to 0.048768

Scyl-Inositol 0.8548 to 0.048975

1-Methyladenosine 0.9104 to 0.003759

Guanine 0.9107 to 0.015678

Creatinine 0.9437 to 0.014959

Table 2B.1: Metabolites that are significantly increased in urine (p value <0.05) among asymptomatic CHF (DCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Furoylglycine 3.7617 up to 7.23E-06

Trans-ferulic acid 2,4374 up to 5.98E-05

Pyrogallol 1.7624 to 0.020466

N-Phenylacetylglycine 1.7323 up to 0.002368

Glycerol-3-phosphate, polar fraction 1.6531 to 1.08E-05

Fructose 1.6453 up to 0.000774

Sorbitol 1,6079 up to 2.44E-06

Lixosa 1,5891 to 8.18E-07

Sucrose 1.5826 up to 0.001411

Pyridoxine 1.5745 to 0.00913

(continuation)

METABOLITE regulation value p value

Arabinose 1.5713 to 1.11E-06

Glycerate 1.5613 up to 0.005009

1,3,7-trimethyluric acid 1,517 up to 0,009369

4-hydroxyhipuric acid 1,4983 up to 0,003154

Glucuronic acid 1.4826 to 0.000413

Threonic acid 1,4493 to 0.002868

Cyclic guanosine monophosphate 1,3984 to 0.000312

(CGMP)

Fumarate 1,3908 up to 1.03E-05

Citramalate 1,3881 up to 0,002158

Choline phosphate 1.3835 up to 0.013819

O-Phosphoethanolamine 1,3305 up to 0,035078

Normetanephrine 1.3221 to 0.000142

Allantoin 1,3197 to 0.000329

Treitol 1.3157 up to 0.007335

Pentoses 1,3095 to 0,014226

Ribonic acid 1.3016 to 0.000117

Malate 1.2958 up to 0.003207

Xylitol 1.2957 to 0.008052

Arabitol 1,294 up to 0.000182

Hill 1,287 to 0,001343

beta-Alanine, lipid fraction 1,2789 to 0.018029

Pyruvate 1,2772 to 0.000933

Norepinephrine (norepinephrine) 1.2518 to 0.008568

Saccharic acid 1.2412 to 0.011337

N2-Acetylillin 1,2356 to 0.012806

Succinate 1.2235 to 0.048655

2-Deoxyribose 1.2212 up to 0.045753

Galactitol 1,2207 to 0.003091

cis-Aconite 1,211 to 0,004821

Pentose acids 1.1973 to 0.027677

Eritrol 1,191 up to 0,00291

4-Hydroxy-3-methoxydendic acid 1,1883 to 0.008707

Sarcosine 1.1864 up to 0.013952

2-O-Methylascorbic acid 1.1788 up to 0.005187

1,1776 isocitrate up to 0.010189

Ribosa 1.1737 to 0.000363

Ornithine 1,162 to 0,036443

Erythronic acid 1.1575 to 0.001277

Xylulose 1,1457 up to 0,006391

Aspartate 1,1274 to 0.021518

Cysteine 1.0931 to 0.044887

cis-4,5-Dihydroxy-1,2-dithian 1.0386 to 0.015889

Table 2B.2: Metabolites that are significantly decreased in urine (p value <0.05) among asymptomatic CHF (DCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Androstenedione 0,3801 up to 7.46E-05

Hippuric acid 0.4043 to 0.000274

7-Methyluric acid 0.453 to 0.000741

7-Methylxanthine 0.5573 up to 0.004553

1-Methyluric acid 0.5644 up to 0.001409

Indole-3-lactic acid 0.6224 to 0.041393

Carnitine 0.6722 to 0.04124

gamma-carboxyglutamate 0.7103 up to 1.56E-06

21-Hydroxyprogesterone (11-0.718 up to 0.042393 Deoxycorticoid ester)

Testosterone 0.7224 to 0.015703

Pantothenic acid 0.7624 to 0.026493

N-Acetylhistidine 0.7804 to 0.000521

2-Methylserine 0.8171 to 0.011802

Argininosuccinate 0.8183 to 0.011006

(continuation)

METABOLITE regulation value p value

Uracil 0.8197 to 0.018975

7-Methylguanosine 0.8246 to 0.001405

5,6,7,8-Tetrahydrobiopterin 0.8526 to 0.038065

Table 3B.1: Metabolites that are significantly increased in urine (p value <0.05) among asymptomatic CHF patients (ICMP) with NYHA I and controls

METABOLITE regulation value p value

Salicylic acid 7.1792 to 0.000364

Furoylglycine 3.5119 up to 1.76E-05

Trans-ferulic acid 2,1585 to 0.000498

N-Methyl-trans-4-hydroxyproline 1,9481 to 0.012202

Sucrose 1,9105 to 6.96E-06

1,3,7-trimethyluric acid 1,7178 to 0.000942

Cyclic guanosine monophosphate 1.6645 to 4.24E-08

(CGMP)

Sorbitol 1,5786 to 4.99E-06

4-Hydroxyhipuric acid 1,532 to 0,001734

Fructose 1.4718 up to 0.008397

Pyridoxine 1.4247 to 0.037875

Lixosa 1.3733 to 0.000485

Glycerol-3-phosphate, polar fraction 1,3656 to 0.005574

Arabinose 1,3498 up to 0.000828

Pentoses 1,3462 up to 0.006581

Glucuronic acid 1.3426 to 0.007562

Threonic acid 1.3178 to 0.025173

Fumarate 1,2778 up to 0.000897

Xylitol 1,2556 to 0,018944

Normetanephrine 1.2349 up to 0.003648

Norepinephrine (norepinephrine) 1.2318 to 0.013981

Pyruvate 1,2259 to 0,005421

2-O-Methylascorbic acid 1.1865 to 0.003698

cis-Aconite 1,1782 to 0.014942

1,1374 isocitrate up to 0.041253

Erythronic acid 1,1301 to 0.006565

Aspartate 1,128 up to 0,020087

Xylulose 1,1199 up to 0,022063

Cysteine 1.1012 to 0.028933

Table 3B.2: Metabolites that are significantly decreased in urine (p value <0.05) among asymptomatic CHF patients (ICMP) with NYHA I and controls

METABOLITE regulation value p value

Indole-3-lactic acid 0.3713 up to 2.21 E-05

Androstenedione 0.3946 to 6.03E-05

Carnitine 0.5107 to 0.000266

O-Acetylcarnitine 0.5237 up to 0.00366

Hippuric acid 0.5763 to 0.025975

Histidine 0.5885 to 0.000375

Pantothenic acid 0.6026 to 3.64E-05

3-hydroxyphenylacetic acid 0.6154 to 0.006824

7-Methylxanthine 0.6484 to 0.030196

Cresol sulfate 0.6616 to 0.016038

Histamine 0.6923 to 0.000703

Dihydroxyindole 0.7087 to 0.031704

Uracil 0.7287 to 0.000189

Testosterone 0.7314 to 0.014325

Glycine 0.7478 up to 0.013642

scyllo-inositol 0.7615 to 0.009741

3,4-Dihydroxyphenylalanine (DOPA) 0.764 to 0.010629

Threonine 0,7882 up to 0,012645

gamma-carboxyglutamate 0.7986 up to 0.001194

2-Methylserine 0.801 to 0.005054

7-Methylguanosine 0.8211 to 0.000928

(continuation)

METABOLITE regulation value p value

Adenine 0.8282 to 0.024676

Serine 0.8389 to 0.02306

N-Acetylhistidine 0.8494 to 0.019689

1-Methyladenosine 0.8754 to 0.001696

Asparagine 0.8934 to 0.033135

Table 4B.1: Metabolites that are significantly increased in urine (p <0.05) among asymptomatic CHF (HCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Salicylic acid 7.3866 to 0.000339

N-Acetylhistamine 1,525 to 0,049761

Pentoses 1.3775 to 0.003943

Fructose 1.3519 to 0.041052

Choline phosphate 1,323 to 0,034719

Lixosa 1.3072 up to 0.004742

Sorbitol 1.3025 to 0.008434

Glucuronic acid 1.2834 to 0.025558

Arabinose 1.2775 to 0.00845

Treitol 1.23 to 0.044172

Xylitol 1.2175 to 0.045281

4-Deoxytronic acid 1,1957 to 0,028122

Ribonic acid 1.1543 to 0.035908

Arabitol 1,1447 to 0,049668

Table 4B.2: Metabolites that are significantly decreased in urine (p value <0.05) among asymptomatic CHF (HCMP) patients with NYHA I and controls

METABOLITE regulation value p value

Indole-3-lactic acid 0.5361 up to 0.007966

Androstenedione 0.604 to 0.033669

Pantothenic acid 0.7116 to 0.005872

Histamine 0.7614 to 0.014648

Uracil 0.7711 to 0.00242

N-Acetylaspartate 0.8426 to 0.008874

Creatinine 0.9255 to 0.017357

Table 5B.1: Metabolites that are significantly increased in urine (p value <0.05) in symptomatic CHF patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Salicylic acid 6,4903 up to 7.95E-05

Furoylglycine 2,2373 to 0.000696

Sucrose 2.0268 up to 9.09E-10

Fructose 1,7023 up to 4.71E-06

Trans-ferulic acid 1.6798 to 0.003762

Sorbitol 1.61 to 2.12E-09

Glucuronic acid 1.5544 to 4.54E-07

Glycerol-3-phosphate, polar fraction 1.4237 up to 6.88E-05

Glucose-1-phosphate 1,389 to 4.31E-05

Cyclic guanosine monophosphate 1.3732 to 3.79E-05

(CGMP)

Arabinose 1,3442 to 5.03E-05

Xylitol 1,3378 up to 0.000126

Lixosa 1,331 to 0.000143

Choline phosphate 1.2863 to 0.014126

2-O-Methylascorbic acid 1,2783 to 1.19E-07

Norepinephrine (norepinephrine) 1.2567 to 0.001171

Normetanephrine 1.2467 to 0.000246

cis-Aconite 1,2435 to 3.77E-05

4-hydroxyphenylacetic acid 1.2415 to 0.018318

N2-Acetylillin 1,2365 to 0.001366

Threonic acid 1,2344 to 0,028086

Pentoses 1,2068 up to 0,026474

(continuation)

METABOLITE regulation value p value

Glucose 1,2005 to 0.009248

Eritrol 1,1991 up to 7,29E-05

Pyruvate 1.1974 to 0.002509

Malate 1.1967 to 0.007089

Ribonic acid 1.1922 to 0.0009

Erythronic acid 1.1824 up to 5.27E-06

Arabitol 1,1808 up to 0,001807

Fumarate 1.1544 to 0.015957

Sarcosine 1.1511 to 0.009384

5-Hydroxy-3-indoleacetic acid (5-HIAA) 1,1471 to 0,029306

Allantoin 1,144 to 0,030492

Saccharic acid 1.1433 to 0.031641

Xylulose 1,1401 to 0.000698

Galactitol 1,1359 up to 0,012471

4-Hydroxy-3-methoxydendic acid 1,1353 to 0.014904

Ribosa 1.1345 to 0.000309

1,1343 isocitrate up to 0.01045

Ornithine 1,1166 to 0,047455

Cysteine 1.1069 up to 0.003324

cis-4,5-Dihydroxy-1,2-dithian 1,0526 to 0.00003

Homoserine 1.0274 to 0.034785

Triethanolamine 1,0265 to 0,027912

Table 5B.2: Metabolites that are significantly decreased in urine (p value <0.05) in symptomatic CHF patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Hippuric acid 0.4517 up to 7.15E-05

Androstenedione 0.4541 to 0.000011

Indole-3-lactic acid 0.48989 up to 0.000241

7-Methylxanthine 0.5029 to 0.000013

7-Methyluric Acid 0.5356 to 0.000725

Histidine 0.5956 to 7.08E-06

1-Methylxanthine 0.6136 to 0.003978

3-Hydroxyhipuric acid 0.6308 to 0.015768

beta-Aminoisobutyrate 0.6394 up to 0.002554

Metixanthin 0.6402 to 0.000227

1 -Metiluric acid 0.6506 to 0.003008

Citrate 0.6507 to 0.000302

Proline betaine 0.6679 to 0.048094

Glycine 0.6698 up to 1.52E-05

21-Hydroxyprogesterone (11- 0.6807 to 0.00206 Deoxycorticoid ester)

3-hydroxyphenylacetic acid 0.6869 up to 0.008055

Uracil 0.693 to 4.42E-08

Pantothenic acid 0.7216 to 0.00071

Carnitine 0.7375 to 0.040028

3,4-Dihydroxyphenylalanine (DOPA) 0.753 to 0.000675

Scilium-Inositol 0,7559 to 0,000962

N-Methylglutamate 0.7799 up to 0.003953

3-O-Methyldopamine 0.7803 up to 0.002571

3,4-Dihydroxyphenylacetic acid 0.7832 up to 0.00238

(DOPAC)

Threonine 0.7902 up to 0.00176

Glycolate 0.8091 to 0.011236

Tryptophan 0.8189 up to 0.005527

2-Methylserine 0.822 to 0.002532

Serine 0.8551 to 0.010459

N-Acetylaspartate 0.8608 up to 0.003192

Phenylalanine 0.8636 to 0.011172

7-Methylguanosine 0.8948 to 0.021033

Valine 0.9018 to 0.039257

Guanine 0.9121 to 0.021192

Creatinine 0.9508 to 0.044878

Table 6B.1: Metabolites that are significantly increased in urine (p value <0.05) in symptomatic DCMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Salicylic acid 4.0279 to 0.014193

Furoylglycine 2,823 to 0.000269

Sucrose 2,1928 to 3.22E-08

Fructose 2,0382 up to 1,04E-06

Trans-ferulic acid 1,954 to 0,001814

Sorbitol 1,7471 to 1.56E-08

Cyclic guanosine monophosphate 1.5926 to 3.61E-07

(CGMP)

Norepinephrine (norepinephrine) 1.5149 to 8.03E-07

Normetanephrine 1.5093 to 1.27E-08

N-Phenylacetylglycine 1.4937 to 0.022009

Glycerol-3-phosphate, polar fraction 1.4499 to 0.000751

3-O-Galactosylglycerol 1.4376 up to 0.013777

Lixosa 1,4327 to 9.16E-05

Pyridoxine 1,425 to 0,033783

4-hydroxyphenylacetic acid 1.4024 up to 0.003295

Glucuronic acid 1,397 to 0,001973

Adrenaline (Epinephrine) 1,3698 to 0.023811

Arabinose 1,353 to 0.000854

Xylitol 1,3295 to 0.002748

Glucose 1.3156 to 0.001729

4-Hydroxy-3-methoxyphenyl glycol 1,2753 to 0.035973

(HMPG)

Threonic acid 1,2704 to 0,046908

cis-Aconite 1,2676 to 0.000344

N2-Acetylillin 1.2639 to 0.004622

4-Hydroxy-3-methoxydendic acid 1,2323 to 0.001114

Fumarate 1,2309 up to 0.003913

2-O-Methylascorbic acid 1,2301 to 0.000336

Malate 1.2213 up to 0.020705

5-Hydroxy-3-indoleacetic acid (5-HIAA) 1,221 to 0.011597

Ribonic acid 1.2069 up to 0.004435

Allantoin 1.2061 to 0.012027

Erythronic acid 1,1999 to 3.95E-05

Homovainillinic Acid (HVA) 1.1943 to 0.048917

Saccharic acid 1.1915 to 0.036953

Sarcosine 1,1903 up to 0.009932

Eritrol 1,1678 up to 0.006485

Pyruvate 1.1648 to 0.032707

Arabitol 1.1621 to 0.023687

Ribosa 1,1452 up to 0,001836

Isocitrate 1.1429 to 0.030486

Xylulose 1.1312 to 0.010856

cis-4,5-Dihydroxy-1,2-dithian 1,0608 up to 0.000121

Homoserine 1.0475 up to 0.002865

Triethanolamine 1.0394 up to 0.00737

Table 6B.2: Metabolites that are significantly decreased in urine (p <0.05) in symptomatic DCMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Indole-3-lactic acid 0.4098 to 9.04E-05

Hippuric acid 0.4225 to 0.00046

Androstenodione 0.4489 up to 0.000845

1-Methyluric acid 0.4844 to 3.45E-05

7-Methylxanthine 0.4924 to 0.000297

7-Methyluric Acid 0.5054 to 0.002697

1-Methylxanthine 0.5284 to 0.002778

Quinic acid (additional: 0.5414 acid up to 0.008261

chlorogenic (CGA))

3,7-dimethyluric acid 0.575 to 0.008169

(continuation)

METABOLITE regulation value p value

3-hydroxyhipuric acid 0.5945 to 0.030901

Histidine 0.619 to 0.000668

Glycine 0.6286 to 0.000168

Creatine 0.6395 to 0.012879

Uracil 0.6566 to 4.93E-07

Metixanthin 0.6603 up to 0.006166

Citrate 0.6722 to 0.009617

21-Hydroxyprogesterone (11-0.7122 to 0.038051 Deoxycorticoid ester)

2-Methylserine 0.721 to 3.26E-05

N-Methylglutamate 0.7235 up to 0.004065

Testosterone 0.7309 up to 0.018184

Glycolate 0.78 to 0.022388

Scyl-Inositol 0.7851 to 0.027144

Threonine 0.7876 to 0.010511

Pantothenic acid 0.7879 to 0.044566

3-O-Methyldopamine 0.7955 to 0.025983

3,4-Dihydroxyphenylacetic acid 0.8036 to 0.029304

(DOPAC)

N-Acetylaspartate 0.8368 up to 0.004747

gamma-carboxyglutamate 0.8723 to 0.044921

Guanine 0.8844 to 0.014406

Table 7B.1: Metabolites that are significantly increased in urine (p value <0.05) in symptomatic ICMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Salicylic acid 9.8395 up to 3.01 E-05

Furoylglycine 3,4785 to 3.09E-05

Trans-ferulic acid 2,2122 to 0.000406

Sucrose 2.1046 to 4.39E-07

Glucuronic acid 1,7374 to 1.22E-06

Sorbitol 1,6029 to 3.65E-06

1,3,7-trimethyluric acid 1.5738 up to 0.008616

Pyridoxine 1.5352 up to 0.0138

Glycerol-3-phosphate, polar fraction 1,4804 to 0.000636

Choline phosphate 1.4651 to 0.004763

Cyclic guanosine monophosphate 1.4456 to 0.000113

(CGMP)

Glucose-1-phosphate 1.4238 up to 0.000446

Fructose 1.4164 up to 0.018873

Xylitol 1.4036 to 0.000637

Lixosa 1.3845 to 0.000446

Arabinose 1,373 to 0.00056

2-O-Methylascorbic acid 1,3491 to 7.17E-07

Pyruvate 1,3343 to 0.000121

cis-Aconite 1.2567 to 0.000915

Eritrol 1,2509 up to 0.000176

Erythronic acid 1,2509 to 1.42E-06

4-Deoxytrreonic acid 1,2346 to 0.009998

Arabitol 1.1871 to 0.013263

Allantoin 1.1729 to 0.039905

Ribonic acid 1,1689 to 0.023055

Glucose-6-phosphate 1,1688 to 0.020676

Sarcosine 1,1647 up to 0,029974

Galactitol 1.1619 to 0.027451

Isocitrate 1.1568 to 0.023505

Xylulose 1,151 to 0,00532

Ribose 1.1411 to 0.003569

Cysteine 1,1329 up to 0,005615

cis-4,5-Dihydroxy-1,2-dithian 1,0516 to 0.001602

Triethanolamine 1.0362 to 0.018018

Homoserine 1.0352 up to 0.032255

Table 7B.2: Metabolites that are significantly decreased in urine (p value <0.05) in symptomatic ICMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Androstenedione 0,3484 up to 2.12E-05

Hippuric acid 0.3568 to 4.33E-05

Indole-3-lactic acid 0.3923 to 8.08E-05

7-Methyluric acid 0.4088 to 0.00017

Histidine 0.4664 to 2.87E-07

7-Methylxanthine 0.4724 to 0.000345

Citrate 0.5219 up to 4.27E-05

3-hydroxyphenylacetic acid 0.5237 to 0.000484

Glycine 0.5323 up to 8.15E-07

21-Hydroxyprogesterone (11-0.5504 to 0.00036 Deoxycorticoid ester)

Pantothenic acid 0.5809 up to 1.42E-05

3-hydroxyhipuric acid 0.5864 to 0.031759

Metixanthin 0.593 to 0.000963

Carnitine 0.6024 to 0.010614

3,4-Dihydroxyphenylalanine (DOPA) 0.6097 to 5.2E-06

1-Methyluric acid 0.6335 to 0.011537

Dihydroxyindole 0.6598 to 0.011005

Uric acid 0.681 to 0.015881

myo-Inositol 0.685 to 0.024313

Uracil 0.6865 to 1.43E-05

scyllo-Inositol 0.6913 to 0.001091

3,4-Dihydroxyphenylacetic acid 0.6988 to 0.000654

(DOPAC)

Tryptophan 0.7001 to 0.000142

3-O-Methyldopamine 0.7017 up to 0.00099

3-Methoxytyrosine 0.7082 to 0.000959

Threonine 0.7165 to 0.000636

Lysine 0.7171 to 0.024902

4-Pyridoxic acid 0.7302 up to 0.005162

Testosterone 0.7449 up to 0.030703

N-Methylglutamate 0.7495 to 0.012824

Serine 0.7526 to 0.000334

Glycolate 0.7544 to 0.011791

Phenylalanine 0.7584 to 0.000217

3-hydroxyisovaleric acid 0.7602 up to 0.019685

Alanine 0,7989 up to 0,015519

3-Hydroxyisobutyrate 0.8165 up to 0.03859

3,4-Dihydroxyphenyl glycol (DOPEG) 0.8225 to 0.028261

N-Acetylaspartate 0.8299 up to 0.004567

Valine 0.8343 up to 0.005576

7-Methylguanosine 0.8358 up to 0.003692

2-Methylserine 0.8472 to 0.043246

Kinurenic acid 0.8542 to 0.019323

Tyrosine 0.856 to 0.018015

Leucine 0.86 to 0.036823

Methionine 0.879 to 0.03827

Table 8B.1: Metabolites that are significantly increased in urine (p value <0.05) in symptomatic HCMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Salicylic acid 6.6437 to 0.002266

Sucrose 1.6282 up to 0.001702

Fructose 1.5919 up to 0.003407

Glucuronic acid 1,5751 to 0.000166

Glucose-1-phosphate 1,5149 up to 0.000168

Sorbitol 1,3809 up to 0,00274

Lactate 1.3635 to 0.009727 Phenylacetylglutamine 1.3406 to 0.025706

N2-Acetyllisin 1.2872 up to 0.006102

(continuation)

METABOLITE regulation value p value

Glycerol-3-phosphate, polar fraction 1.2726 to 0.048008

Xylitol 1,2607 up to 0,02801

Lixosa 1,2579 up to 0,022204

Arabinose 1,2576 to 0.022639

2-O-Methylascorbic acid 1,2561 to 0.00036

Malate 1,2492 up to 0,013528

Glucose 1.2318 to 0.03164

Sulfate 1.2195 to 0.024878

Normetanephrine 1.1942 to 0.024614

Arabitol 1,1922 to 0,017429

Ribonic acid 1,1885 to 0.018596

Pyruvate 1,1881 up to 0,030075

cis-Aconite 1.1788 to 0.024711

Eritrol 1,1757 up to 0,011389

Galactitol 1.1724 to 0.021204

Erythronic acid 1,1451 to 0,005636

Cysteine 1,1387 up to 0,006907

Xylulose 1,131 to 0,022762

cis-4,5-Dihydroxy-1,2-dithian 1,039 to 0,023944

Table 8B.2: Metabolites that are significantly decreased in urine (p value <0.05) in symptomatic HCMP patients with NYHA II or NYHA III versus controls

METABOLITE regulation value p value

Androstenedione 0.48985 to 0.005404

Hippuric acid 0.5314 to 0.017002

7-Methylxanthine 0.5712 up to 0.009908

beta-Aminoisobutyrate 0.576 to 0.008057

Metixanthin 0.6784 to 0.021163

Xanthine 0.7526 to 0.035361

Uracil 0.7635 up to 0.003284

Histamine 0.7656 to 0.022879

Scilium-Inositol 0.7866 to 0.035332

1-Methyladenosine 0.9128 to 0.047374

Creatinine 0.9136 to 0.008178

Table 9B.1: Metabolites showing progressive increase in controls on NYHA I to NYHA III in urine of CHF patients and controls

ratio (front control) regulation (front control) p-value (front control)

METABOLITE

Sucrose 1.5739 1.903 2.1788 up to 3.98E-05 8.06E-07 6.79E-09 Acid

glucuronic

1.3567 1.5129 1.6053 up to 0.000293 3.08E-05 3.43E-06 Fructose 1.5122 1.641 1.7431 up to 0.000227 0.000131 0.000042

Table 13B.1: Metabolites showing a significantly positive correlation (p <0.05) with plasma LVEF of CHF patients and controls

METABOLITE estimate p value correlation

Hippuric acid 1,529516 positive 2.68E-06

Androstenodione 1.487558 positive 3.76E-06

7-Methyluric acid 1.401171 positive 8.24E-05

Indole-3-lactic acid 1.382959 positive 0.000475

Histidine 1,322863 positive 1.62E-07

7-Methylxanthine 1,322406 positive 0.000124

O-Acetylcarnitine 1.274541 positive 0.003596

Carnitine 1.272619 positive 0.000514

1 -Metiluric acid 1.231272 positive 0.002592

3,4-Dihydroxyphenylalanine (DOPA) 1,205536 positive 7.71 E-07

Glycine 1,205223 positive 4.08E-06

Citrate 1,197805 positive 0.00045

Uric acid 1,196162 positive 0.001527

Pantothenic acid 1,188484 positive 0.000115

Metixanthin 1.171143 positive 0.006775

Threonine 1,162344 positive 8.37E-06

Lysine 1.161343 positive 0.003507

3-hydroxyisovaleric acid 1,159363 positive 8.58E-05

Uracil 1.156814 positive 1.63E-06

21-Hydroxyprogesterone (11-Deoxychoesterone) 1,1557 positive 0.007924

Table 13B.2: Metabolites showing a significantly negative correlation (p <0.05) with plasma LVEF of CHF patients and controls

METABOLITE ___________________ estimate ______ correlation ______ p value

Furoylglycine 0.687448 negative 0.000503

Trans-ferulic acid 0,76225 negative 0.000699

Sucrose 0.762679 negative 9.07E-07

Cyclic Guanosine Monophosphate

(CGMP) 0.7797882 negative 1.47E-10

Fructose 0.840587 negative 0.002537

Glycerol-3-phosphate, polar fraction 0.850218 negative 7.94E-05

Normetanephrine 0.866303 negative 4.53E-08

Table 14: Chemical / physical properties of the selected analytes. These biomarkers are characterized herein by chemical and physical properties.

continuation

Claims (14)

1. A procedure to diagnose heart failure in a subject, comprising the stages of: a) determine, in a sample of a subject suspected of having heart failure, the amount of at least one biomarker, being at least one biomarker (i) SM_Sphingomyelin (d18: 1, C16: 0) or (ii) SM_Sphingomyelin ( d18: 1, C16: 0) and at least one biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1 , 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1,6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1 or 8B2; b) compare the amount of said at least one biomarker with a reference, whereby heart failure is to be diagnosed. 2. The method of claim 1, wherein said subject suffers from asymptomatic heart failure and the at least one biomarker is a biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1,2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1,4A2, 4B1 or 4B2. 3. The method of claim 2, wherein said asymptomatic heart failure in the subject is heart failure according to NYHA class I. 4. The method of claim 2 or 3, wherein said asymptomatic heart failure is DCMP and said at least one biomarker is selected from the related biomarkers in Table 2A1,2A2, 2B1 or 2B2; or wherein said asymptomatic heart failure is ICMP and at least one biomarker is selected from the related biomarkers in Table 3A1, 3A2, 3B1 or 3B2; or wherein said asymptomatic heart failure is HCMP and said at least one biomarker is selected from the related biomarkers in Table 4A1,4A2, 4B1 or 4B2. 5. The method of claim 1, wherein said subject suffers from symptomatic heart failure and the at least one biomarker is a biomarker selected from the related biomarkers in Table 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1 or 8B2. 6. The method of claim 5, wherein said symptomatic heart failure is heart failure according to NYHA class II and / or III. 7. The method of claim 5 or 6, wherein said symptomatic heart failure is DCMP and said at least one biomarker is selected from the related biomarkers in Table 6A1,6A2, 6B1 or 6B2; or wherein said symptomatic heart failure is ICMP and said at least one biomarker is selected from the related biomarkers in Table 7A1, 7A2, 7B1 or 7B2; or wherein said symptomatic heart failure is HCMP and said at least one biomarker is selected from the related biomarkers in Table 8A1, 8A2, 8B1 or 8B2. 8. Use of (i) SM_Sphingomyelin (d18: 1, C16: 0) or (ii) SM_Sphingomyelin (d18: 1, C16: 0) and at least one biomarker selected from the related biomarkers in Table 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1,6B2, 7A1, 7A2, 7B1, 7B2, 7B1, 7B2 8A1, 8A2, 8B1 or 8B2 in a sample of a subject to diagnose heart failure. 9. A method of identifying if a subject needs a heart failure treatment comprising the steps of any one of claims 1 to 8; and the additional stage of identifying a subject who needs a treatment for heart failure if heart failure has been diagnosed. 10. A diagnostic device comprising: a) an analysis unit comprising a detector for (i) SM_Sphingomyelin (d18: 1, C16: 0) or (ii) SM_Sphingomyelin (d18: 1, C16: 0) and at least one biomarker as related in any one of Tables 1A1, 1A2, 1B1, 1B2, 2A1, 2A2, 2B1, 2B2, 3A1, 3A2, 3B1, 3B2, 4A1, 4A2, 4B1, 4B2, 5A1, 5A2, 5B1, 5B2, 6A1, 6A2, 6B1, 6B2, 6B1, 6B2 7A1, 7A2, 7B1, 7B2, 8A1, 8A2, 8B1, 8B2, 9A1, 9A2, 9B1, 9B2, 10A1, 10A2, 10B1, 10B2, 11A1, 11A2, 11B1, 11B2, 12A1, 12A2, 12B1, 12B2, 13B2, 13B2, 13A 13A2, 13B1 or 13B2, wherein said analysis unit is adapted to determine the amount of said biomarker detected by the detector, and, operatively linked to the previous one; b) an evaluation unit comprising a computer, comprising a tangible computer program code included to carry out a comparison of the determined amount of the at least one biomarker and a reference amount and a database comprising said reference amount as for said biomarker, by which it will be diagnosed if a subject suffers from heart failure. 11. A procedure for controlling the progression or regression of heart failure in a subject comprising the stages of: a) determine in a first and second sample of said subject the amount of at least one biomarker, said at least one biomarker (i) SM_Sphingomyelin (d18: 1, C16: 0) or (ii) SM_Sphingomyelin (d18: 1, C16: 0) and at least one biomarker selected from the biomarkers listed in Table 9A1, 9A2, 9B1, 9B2, 10A1, 10A2, 10B1, 10B2, 11A1, 11A2, 11B1, 11B2, 12A1, 12A2, 12B1, 12B2, 13A1, 13A2, 13B1 or 13B2, in which said first sample was obtained before said second sample ; Y b) compare the amount of at least one biomarker determined in the first sample with the amount determined in the second sample, so the progression or regression of heart failure will be diagnosed. 12. The method of claim 11, wherein said heart failure is DCMP and said at least one biomarker is selected from the biomarkers listed in Table 10A1, 10A2, 10B1 or 10B2; or wherein said heart failure is ICMP and said at least one biomarker is selected from the biomarkers listed in Table 11A1, 11A2, 11B1 or 11B2; or wherein said heart failure is HCMP and said at least one biomarker is selected from the biomarkers listed in Table 12A1, 12A2, 12B1 or 12B2. 13. The method of claim 11 wherein said progression or regression of the heart failure is accompanied by the progression or regression of LVE reduced ^f and said at the least one biomarker selected from the biomarkers listed in Table 13A1, 13A2, 13B1 or 13B2. 14. The method, use, data collection or diagnostic device according to any one of claims 1 to 13, wherein said sample is a sample of body fluid, preferably a sample of blood, plasma or serum, more preferably it is a plasma sample.