Use of the relative gene expression level value of gene hmga2 in the prognosis or diagnosis of a fatty liver disease of a subject

Abstract

The invention relates to the use of the relative gene expression level value of the gene HMGA2 and / or of a gene the expression of which statistically correlates with that of the gene HMGA2, in the prognosis and / or diagnosis of a fatty liver disease of a subject.

Description

[0001] Lipocyte BioMed GmbH Erbrichterweg 7a, 28357 Bremen Use of the relative value of the gene expression level of the HMGA2 gene in the prognosis or diagnosis of fatty liver disease in a test subject The present invention relates to the use of the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression statistically correlates with that of the HMGA2 gene in the prognosis and / or diagnosis of fatty liver disease in a test subject. The invention particularly relates to a corresponding use for distinguishing between non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) as well as a corresponding use for prognosticating the deterioration of the liver condition. The invention also relates to a method for prognosticating and / or diagnosing a liver disease.Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in Germany, with a rising prevalence. NAFLD specifically includes steatosis hepatis (fatty liver) and is a precursor to non-alcoholic steatohepatitis (NASH; i.e., fatty liver with inflammation and liver cell necrosis) and liver cirrhosis. NAFLD occurs in all age groups and is found in 14 to 30% of the general population [Browning J, Szczepaniak L, Dobbins R, Nuremberg P, Horton JD, Cohen JC, Grundy SM, Hobbs HH. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology 2004; 40: 1387–95; Nomura H, Kashiwagi S, ^. *20 ^ 23 ^ 26 ^ 55 ^ 60 ^ * ^^^^^^^Hayashi J, Kajiyama W, Tani S, Goto M. Prevalence of fatty liver in a general population of Okinawa, Japan. Jpn J Med 1988; 27: 142–9.]. The prevalence increases even further in patients with higher body weight. Steatosis hepatis can be diagnosed in approximately 66% of patients with a BMI ≥ 30 kg / m² and in over 90% of patients with a BMI > 39 kg / m² [Angulo P Nonalcoholic fatty liver disease. N Engl J Med. 2002; 346: 1221–31.]. Worldwide, the prevalence of NAFLD is 20–30% in countries with a Western lifestyle, such as Europe, the USA, Latin America, China, and Japan. The prevalence of NASH in these countries is between 3–16%. The pathogenesis of NAFLD is far from fully understood, but a "two-hit model" hypothesis is proposed that leads to the development of NAFLD. Fat accumulation in the liver is closely linked to insulin resistance.The latter leads to activation of lipolysis in peripheral adipose tissue, resulting in increased influx of free fatty acids (FFA) into the liver. In addition, insulin resistance causes increased de novo synthesis of triglycerides and downregulation of β-oxidation within the liver, which then leads to intrahepatic accumulation of triglycerides (“first hit”) [Browning JD, Horton JD. Molecular mediators of hepatic steatosis and liver injury. J Clin Invest 2004; 114: 147–52.]. The “second hit” encompasses mechanisms that lead to the development of inflammation and fibrosis [Day CP, James OF. Steatohepatitis: a tale of two “hits”? Gastroenterology 1998; 114: 842–5.]. Different factors such as oxidative stress, mitochondrial abnormalities and hormonal disorders are considered responsible for this [Li Z, Yang S, Lin H, Huang J, Watkins PA, Moser AB, Desimone C, Song XY, Diehl AM.Probiotics and antibodies to TNF inhibit inflammatory activity and improve nonalcoholic fatty liver disease. Hepatology 2003; 37: 343–50.; Haque M, Sanyal AJ. The metabolic abnormalities associated with non-alcoholic fatty liver disease. Best Pract Res Clin Gastroenterol 2002; 16: 709–31.]. The disease course and prognosis differ between NAFLD and NASH patients. While NAFLD often has a mild course, NASH shows hepatic necroinflammation and increasing fibrosis, which can progress to liver cirrhosis. In 5–20% of patients with NAFLD, the disease progresses to NASH, and in 10–20% of NASH patients, the disease progresses via fibrosis to liver cirrhosis (< 5%) [Vernon G, Baranova A, Y- ounossi ZM. Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Aliment Pharmacol Ther. 2011; 34: 274–85].The incidence of hepatocellular carcinoma (HCC) in patients with NASH and cirrhosis is 2.6–11.3% [Baffy G, Brunt EM, Caldwell SH. Hepatocellular carcinoma in non-alcoholic fatty liver disease: an emerging menace. J Hepatol. 2012; 56: 1384–91], but NAFLD patients without cirrhosis can also be affected by HCC. Due to the increasing prevalence of NASH liver cirrhosis in the USA, but also in Germany, experts believe this indication will be the main reason for liver transplantation in the 2020s. Experts recommend focusing on NAFLD in its early stages to prevent NASH-associated complications. Because the development of NAFLD is often associated with insulin resistance, it frequently occurs in metabolic disorders such as obesity, diabetes mellitus, and hyperlipidemia. Most patients with NAFLD are clinically asymptomatic.Laboratory abnormalities are often the only evidence of NAFLD, with the most common changes in pure fatty liver disease being an increase in gamma-GT and in NASH an increase in transaminases (AST, ALT). The diagnosis of NAFLD requires clear evidence of hepatic steatosis, which is made either by imaging techniques (e.g., sonography, CT, or MRI) or by liver biopsy. Abdominal sonography is a relatively inexpensive examination, which is why it is often used as a screening method. However, its sensitivity is too low to detect even minimal changes (< 30%) in obese patients [ Mottin CC, Moretto M, Padoin AV, Swarowsky AM, Toneto MG, Glock L, Repetto G. The role of ultrasound in the diagnosis of hepatic steatosis in morbidly obese patients. Obes Surg 2004; 14: 635–7.].Progressive fibrosis can be determined serologically using the FIB4 score, which is calculated from age, platelet count, and transaminases (ALT, AST). The FIB4 score has a good negative predictive value for excluding advanced fibrosis. However, the positive predictive value of the FIB4 score has not yet been clearly proven scientifically. The gold standard for diagnosing NAFLD remains liver biopsy. Furthermore, biopsy is currently the only examination method capable of distinguishing between simple steatosis and NASH and quantifying the extent of existing fibrosis [Saadeh S, Younossi ZM, Remer EM, Gramlich T, Ong JP, Hurley M, Mullen KD, Cooper JN, Sheridan MJ. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology 2002; 123: 745–50.].However, their routine use is highly controversial because NAFLD usually has a benign course, no established therapies for NAFLD are available, and, above all, the biopsy itself is a diagnostic procedure associated with considerable risks [Dienstag JL. The role of liver biopsy in chronic hepatitis C. Hepatology 2002; 36 (Suppl 1): pp. 152–60]. This is because a liver biopsy is not only associated with considerable morbidity (postinterventional pain in 10–30%), but is also associated with the risk of worsening liver fibrosis and even a certain mortality (approximately 0.1–0.01%) [Stauber R. Noninvasive diagnosis of liver fibrosis in chronic hepatopathies. J Gastroenterol Hepatol Erkr 2019; 7: 12–17].Furthermore, a fundamental problem is that the histopathological structures of NAFLD / NASH can be unevenly distributed in the liver, so that histological diagnosis based on liver biopsy can be associated with considerable sampling error [Ratziu V, et al. Sampling variability of liver biopsy in non-alcoholic fatty liver disease. Gastroenterology 2005; 128: 1898-1906]. Currently, NAFLD therapy focuses primarily on the components of the metabolic syndrome, particularly insulin resistance, since steatosis hepatis alone probably does not justify treatment. Previous therapeutic attempts have focused on various aspects of the disease, with particular emphasis on identifying and treating underlying metabolic disorders, such as diabetes mellitus and hyperlipidemia.Attempts have been made to improve insulin sensitivity in these patients through weight reduction, increased exercise, or various drug therapies. Liver-protective substances such as antioxidants have been used to protect the liver from the “second hit.” The majority of patients with NAFLD are obese with increased visceral fat mass, which is associated with increased insulin resistance. For this reason, weight reduction is the logical first-line therapy for such patients. However, weight should not decrease by more than 0.5–1 kg / week, as excessively rapid weight loss (e.g., after bariatric surgery) can lead to a significant worsening of existing steatohepatitis [Andersen T, Gluud C, Franzmann MB, Christoffersen P. Hepatic effects of dietary weight loss in morbidly obese subjects. J Hepatol 1991; 12:224–9.;Luyckx FH, Scheen AJ, Desaive C, Dewe W, Gielen JE, Lefebvre PJ.Effects of gastroplasty on body weight and related biological abnormalities in morbid obesity. Diabetes Meta- tab 1998; 24: 355–61.] The object of the present invention, based on the prior art, was to provide a reliable method for diagnosing and / or prognosticating liver diseases. In particular, the goal was to determine reliable diagnostic or prognostic values ​​without the need for a (risky) liver biopsy. Furthermore, it was preferred to differentiate between certain forms of liver disease within the framework of diagnosis, and it was also preferred to enable statements about the potential progression of existing liver diseases within the framework of prognosis. This object is achieved by using the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression statistically correlates with that of the HMGA2 gene in the prognosis and / or diagnosis of fatty liver disease in a test subject.Surprisingly, it was found that HMGA2 gene expression in adipose tissue is associated with the progression of liver fibrosis and plays an important role in differentiating between the severity of liver damage and in the classification of NAFLD and NASH (see below). This association between HMGA2 expression and liver disease is completely new and has not been scientifically described. “HMGA2” in the sense of this text is the High Mobility Group AT-Hook Protein 2 (HMGA2) or its gene or the associated mRNA and / or parts of this protein or gene (or its mRNA), preferably at least one amino acid chain of ≥ 7 amino acids, more preferably ≥ 15 amino acids and particularly preferably ≥ 20 amino acids or a nucleic acid chain of ≥ 20 nucleic acids, more preferably ≥ 40 nucleic acids and particularly preferably ≥ 55 nucleic acids, if applicable per strand.HMGA2 is a transcription factor that influences the regulation of gene expression and belongs to the group of high mobility group A proteins (HMGA proteins). HMGA proteins are chromatin-associated, acid-soluble non-histone proteins that bind to sequence-independent, specific DNA motifs. As architectural transcription factors, they increase or inhibit the binding capacity of other transcription factors through structural changes in chromatin organization. The human HMGA2 gene is located in the chromosomal region 12q14~15 and consists of five exons spanning a region ≥160 kb in length. It encodes a 109 amino acid protein with a molecular mass of 12 kDa. The HMGA2 protein is characterized by three highly conserved DNA-binding domains, the so-called AT hooks, and an acidic, negatively charged, C-terminal domain.A gene whose expression is "statistically correlated" with that of another (named) gene within the meaning of this text is one in which there is a mathematical relationship between the gene expression of the affected gene and that of the named gene. Preferably, the expression of these two affected genes correlates linearly. The value for the relative gene expression level within the meaning of the present invention can be determined in any way known to the person skilled in the art. Preference is given to determining the gene expression level at the mRNA level or at the protein level. The mRNA level is preferred. In the present invention, relative gene expression levels are determined. Where only "gene expression level" is mentioned below, these always refer to relative gene expression levels, unless otherwise stated.The relative gene expression levels are preferably determined by determining the gene expression level of the gene to be investigated in relation to the expression level of a housekeeping gene, preferably selected from the group consisting of HPRT, 18S rRNA, GAPDH, GUSB, PBGD, B2M, ABL, RPLP0, with HPRT being particularly preferred. A preferred method for determining the relative gene expression level is described in Schmittgen, Thomas D and Livak, Kenneth J, 2008, Analyzing real-time PCR data by the comparative C. Tmethod (Nature Protocols 3: 1001–1008). The term “prognosis” as used in this text means a prediction of an increased probability of developing or occurring a clinical condition or disease. The term “diagnosis” of a disease as used in this text means that a disease that already exhibits clinical symptoms is identified and / or confirmed. Non-alcoholic fatty liver disease is also abbreviated to NAFLD in the text below. Non-alcoholic steatohepatitis is also abbreviated to NASH in the text below. “Subject” as used in this application is defined as humans and animals, with humans being the preferred option.According to the invention, a use according to the invention is preferred, wherein the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression statistically correlates with that of the HMGA2 gene is related to the relative value of the gene expression level of the ADIPOQ gene and / or a gene whose expression correlates with that of the ADIPOQ gene. Accordingly, “adiponectin” (ADIPOQ) within the meaning of this text is the adiponectin protein or its gene or the associated mRNA and / or parts of this protein or gene (or its mRNA), preferably at least one amino acid chain of ≥ 7 amino acids, more preferably ≥ 15 amino acids and particularly preferably ≥ 20 amino acids or a nucleic acid chain of ≥ 20 nucleic acids, more preferably ≥ 40 nucleic acids and particularly preferably ≥ 55 nucleic acids, if applicable per strand.Adiponectin is an important adipokine involved in the control of lipid metabolism and insulin sensitivity, and has direct anti-diabetic, anti-atherogenic, and anti-inflammatory effects. It stimulates AMPK phosphorylation and activation in the liver and skeletal muscle, thereby increasing glucose utilization and fatty acid combustion. The human adiponectin gene is located in the chromosomal region 3q27 and consists of three exons spanning a region ≥17 kb in length. It encodes, among other things, a 244-amino-acid complete adiponectin protein with a molecular mass of 30 kDa. The adiponectin protein is characterized by a carboxy-terminal globular domain and a collagenous domain at the amino-terminal end. Basically, adiponectin occurs in plasma as a complete protein (244 amino acids) and as a proteolytic cleavage product fragment, also called globular adiponectin.The isoforms of adiponectin arise from different linkages of the globular and collagen domains. Three main complexes circulate in plasma: a low-molecular-weight trimer (LMW), a medium-molecular-weight hexamer (MMW), and a high-molecular-weight complex (HMW). "Relationship" in the sense of this text means that additional knowledge is gained from the combination of the "relationship" values. "Relationship" in the sense of the present invention can particularly mean a comparison with existing databases or the implementation of a statistical method. A preferred statistical method is described below. It has been found that, in the use according to the invention, the inclusion of the relative value of the gene expression level of the ADIPOQ gene leads to an improved prognostic or diagnostic result.A use according to the invention is preferred, wherein the relative value of (i) the gene expression level of the HMGA2 gene or (ii) the HMGA2 gene and the ADIPOQ gene is further related to the relative value of one or preferably both gene expression levels of the genes selected from the group consisting of PPARgamma and IL-6. “PPAR-gamma” in the sense of this text is the peroxisome proliferator-activated receptor gamma (PPAR-gamma), preferably PPAR-gamma isoform 2 or its gene or the associated mRNA and / or parts of this protein or gene (or its mRNA), preferably at least one amino acid chain ≥ 7 amino acids, more preferably ≥ 15 amino acids and particularly preferably ≥ 20 amino acids or a nucleic acid chain of ≥ 20 nucleic acids, more preferably ≥ 40 nucleic acids and particularly preferably ≥ 55 nucleic acids, if applicable per strand.PPAR-gamma is a ligand-binding nuclear transcription factor of the PPAR subfamily, which belongs to the group of nuclear hormone receptors. Through heterodimerization with the retinoid X receptor α (RXRα), PPAR-gamma activates the transcription of various genes. The human PPAR-gamma gene is located in chromosomal band 3p25 and consists of 11 exons. The human PPAR-gamma gene encodes three isoforms, each representing a 477-, a 505-, and a 186-amino acid protein. “IL-6” in the sense of this text is the interleukin-6 or its gene or the associated mRNA and / or parts of this protein or gene (or its mRNA), preferably at least one amino acid chain of ≥ 7 amino acids, more preferably ≥ 15 amino acids and particularly preferably ≥ 20 amino acids or a nucleic acid chain of ≥ 20 nucleic acids, more preferably ≥ 40 nucleic acids and particularly preferably ≥ 55 nucleic acids, if applicable per strand.Interleukin-6 is a cytokine that plays a role in both inflammatory reactions and the maturation of B lymphocytes. Furthermore, it has been shown to be an endogenous inflammatory substance, a so-called pyrogen, which can trigger high fever in the presence of autoimmune diseases or infections. The protein is predominantly produced at sites of acute or chronic inflammation, from where it is secreted into the serum and triggers an inflammatory response via the interleukin-6 receptor alpha. Interleukin-6 is involved in various inflammation-associated disease states, including a predisposition to diabetes mellitus and systemic juvenile idiopathic arthritis (Still's syndrome). The human IL-6 gene is located in chromosomal band 7p15.3 and consists of six exons. The IL-6 precursor protein consists of 212 amino acids.After cleavage of a 28-amino acid signal peptide, the mature interleukin-6 has a length of 184 amino acids (Hirano T, Yasukawa K, Harada H, Taga T, Watanabe Y, Matsuda T, Kashiwamura S, Nakajima K, Koyama K, Iwamatsu A, et al., 1986. Complementary DNA for a novel human interleukin (BSF-2) that induces B lymphocytes to produce immunoglobulin. Nature 324: 73-76). It has again been shown that incorporating the relative value of the gene expression level of the PPAR-gamma and / or IL-6 gene into the inventive use leads to further improved prognostic and diagnostic results.According to the invention, a use according to the invention is preferred, wherein one or more factors selected from the group consisting of BMI, gender, DeRitis quotient, age, FIB4 score, HbA1c value, and serum concentration of one or more enzymes, in turn selected from the group consisting of alanine aminotransferase, aspartate aminotransferase, glutamate dehydrogenase, gamma-glutamyltransferase, and alkaline phosphatase, are further correlated. In principle, it is also possible to measure the corresponding enzyme concentrations in (whole) blood. The BMI, the body mass index, is defined as: body mass [kg] / body height [m]. 2The De Ritis quotient in the sense of the present application is defined as the ratio of the serum concentrations in [U / l] of the enzymes aspartate aminotransferase and alanine aminotransferase, i.e., AST / ALT (or GOT / GPT) [De Ritis F, Coltorti M, Giusti G. An enzymic test for the diagnosis of viral hepatitis; the transaminase serum activities. Clin Chim Acta. 1957 ;2(1):70-4.] The FIB4 score in the sense of this application is defined as follows: FIB-4 = (age [years] x AST [U / l]) / (platelet count [10 9 / l] x √ALT [U / l]) [Sterling RK, Lissen E, Clumeck N, Sola R, Correa MC, Montaner J, S Sulkowski M, Torriani FJ, Dieterich DT, Thomas DL, Messinger D, Nelson M; APRICOT Clinical Investigators. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV / HCV coinfection. Hepatology.2006 ; 43(6): 1317-25.] Serum concentration of aspartate aminotransferase in the sense of this text is the concentration of the enzyme aspartate aminotransferase (ASAT; also aspartate transaminase, AST; also glutamate oxaloacetate transaminase, GOT) determined in the serum in the unit U / l. Serum concentration of alanine aminotransferase in the sense of this text is the concentration of the enzyme alanine aminotransferase (ALAT; also alanine transaminase, ALT; also glutamate pyruvate transaminase, GPT) determined in serum in the unit U / l.Serum concentration of glutamate dehydrogenase, as defined in this text, is the concentration of the enzyme glutamate dehydrogenase (GLDH or GDH) determined in serum in units of U / l. Serum concentration of gamma-glutamyltransferase, as defined in this text, is the concentration of the enzyme gamma-glutamyltransferase (γ-GT) determined in serum or in heparin or EDTA plasma in units of U / l. Serum concentration of alkaline phosphatase, as defined in this text, is the concentration of alkaline phosphatases (AP) determined in serum in units of U / l. The HbA1c value, as defined in this application, is the percentage of glycated hemoglobin in total hemoglobin. It has been shown that relating the relative gene expression level of the HMGA2 gene to one or more of the factors mentioned here leads to an even better prognosis or diagnosis.In this context, it is of course preferred that one or more of the values ​​of the relative gene expression level of the genes mentioned above are also included. According to the invention, a use is preferred wherein the relative value of the gene expression level or the relative values ​​of the gene expression levels are determined in vitro from a fatty tissue sample. In the preferred use according to the invention, the composition of the individual's fatty tissue from mature and immature fat cells, as well as its functionality and, if applicable, degree of inflammation, is indirectly identified based on the gene expression analysis. For example, without being bound to these theories, low levels of PPAR-gamma expression and high levels of HMGA2 expression indicate an increased proportion of immature fat cells in the fatty tissue.Conversely, high PPAR-gamma expression and low HMGA2 expression indicate an increased proportion of mature fat cells in the adipose tissue. Low ADIPOQ expression indicates lower functionality of the adipose tissue. This could be due to a predominance of immature fat cells or the fat cells being in a hypertrophic state, i.e., the fat cells are overloaded with triglycerides and are no longer able to produce the protein adiponectin encoded by ADIPOQ. High IL-6 expression is associated with high inflammation in the adipose tissue, whereas low IL-6 expression levels are more indicative of anti-inflammatory processes in the adipose tissue. By combining the gene expression of these genes, it is possible to make statements about the adipose tissue composition, functionality, and inflammatory processes in the adipose tissue of an individual. Based on this combination of values, surprisingly precise conclusions can be drawn about, among other things,Liver damage, progression of liver fibrosis and the presence of NASH. Preference is given to a use according to the invention wherein the relative value of the gene expression level or the relative values ​​of the gene expression level are determined in vitro from a fat sample, wherein the sample was obtained by puncturing abdominal fatty tissue, preferably subcutaneous abdominal fatty tissue. "In vitro" means that, within the meaning of the present text, the actual sampling from the test subject is not part of the use according to the invention or the method according to the invention (see below). By fan-shaped puncturing with negative pressure, cells and cell clusters can preferably be obtained, which enable molecular genetic analysis.The fan-shaped puncture procedure, on the one hand, reduces the risk of the cannula tip sticking to / blocking with fat cells, and on the other hand, cells from different regions of the fatty tissue in question are obtained, thus providing a representative cross-section of the distribution of different fatty tissue cell types. A use according to the invention is particularly preferred, wherein the sample mass for the fatty tissue samples is ≤ 100 mg, preferably ≤ 50 mg, more preferably ≤ 20 mg, and even more preferably ≤ 5 mg. Surprisingly, it has been shown that reliably differentiated results can be achieved even with very small sample volumes from fatty tissue. It is particularly preferred that the sample was obtained by puncture as a fine-needle aspirate.From the inventors' point of view, determining the respective parameters from fatty tissue also has the following advantages in terms of prognosis: Firstly, after determining the parameters from fatty tissue, individuals who are already suffering from NAFLD or NASH can be identified according to the invention (see also below). Secondly, determining the parameters from fatty tissue according to the invention makes it possible to identify individuals at an earlier stage who have an increased probability of developing NAFLD, NASH or liver fibrosis than, for example, conventional FIB4 score values ​​or the De-Ritis quotient (see also below). According to the invention, the test subject is preferably a human, since a differentiated prognosis and diagnosis in the case of liver disease in humans is of particular importance both economically and from a health policy perspective.As already indicated above, it is preferred that the gene expression level is determined at the mRNA level. This allows reliable data to be obtained with extremely small sample quantities and using established methods. A use according to the invention is preferred, wherein the value or values ​​for the relative gene expression levels and optionally one or more factors, as mentioned above, are related using the multivariate model of the self-organizing maps according to Kohonen (SOM). Statistical methods are described below which are suitable alone or in combination for relating within the meaning of the present invention. The SOM method is also described here: The aim of the method used is to classify (cluster) liver diseases based on various biomarkers such as, for example,HMGA2, ADIPOQ, IL-6 or PPAR-gamma as well as markers described above, which will expand existing classifications both diagnostically and therapeutically in the future. The usual methods of descriptive statistics are used to formally describe the study data. Absolute and relative frequencies are given for nominal parameters, and the median is also given for ordinal parameters. The mean and standard deviation are calculated for metric values. Normal distributions are checked using the Kolmogorov-Smirnov test (KS test). Non-parametric correlations between the biomarkers are calculated using Kendall's tau-b. The χ2 test is used for comparisons between categorical variables. Self-organizing maps (SOM) are used to calculate clusters that are not known a priori. SOMs (here Kohonen maps according to Teuvo Kohonen, see Teuvo Kohonen: Self-Organizing Maps.Springer-Verlag, Berlin 1995, ISBN 3-540-58600-8) are types of artificial neural networks with an unsupervised learning process with the goal of achieving a topographical feature map in the form of clusters of the input space (patient data). Patients (test subjects) within a cluster should be maximally homogeneous and maximally inhomogeneous between clusters. SOMs are used for clustering, visualizing complex relationships, prediction (evaluation), modeling, and data exploration. The network used here consists of 1000 neurons; correlations are automatically compensated, and missing values ​​are taken into account. The SOM-WARD clustering method (2-stage hierarchical clustering algorithm) is used to generate the clusters. Color coding is performed using heat maps. The resulting clusters are compared descriptively. To describe the clusters using decision trees orVarious classification algorithms are used to calculate facts and rules, such as C5.0, CART, and Exhausted Chaid. The quality measures evaluated for the various classifiers were classification accuracy, model compactness (e.g., decision tree size), model interpretability, efficiency, and robustness to noise and missing values. To further validate the models and calculate the importance of biomarkers for the various classification models, RBF networks (radial basis function networks) are created as a prediction model. RBF networks provide a suitable approximation of the cluster assignment of the SOMs. The input vectors are normalized (subtraction of the mean and division by the range (x-min) / (max-min); normalized values ​​range between 0 and 1). The softmax function σ is used as the activation function as a normalized radial basis function.Softmax σ maps a k-dimensional vector z to a k-dimensional vector σ(z). The network performance (how "good" is the network) is checked using the following data: ^ Model summary: Results including error, relative error, or percentage of false predictions. ^ Classification results: A classification table is provided for each dependent variable. ^ ROC curves: ROC curves (Receiver Operating Characteristic curves) indicate the sensitivity and specificity for each possible cutpoint of the input variable. The area under the curve (AUC) is a measure of the quality of the classification. as well as ^ Cumulative gain plots. The following methods are used: 1. Self-organizing neural networks ( ^ Kohonen maps) 2. Classification algorithms ^ Entropy-based learning methods (C5.0) ^ Exhausted Chain and ^ CART 3. Radial basis functions (special type of neural network) 4.Descriptive and inductive statistics 1) Self-organizing neural networks Self-organizing maps (SOMs) are types of artificial neural networks with an unsupervised learning process aimed at achieving a topological representation of the input space (here, patient data). The best-known SOMs are the topology-preserving Kohonen maps developed by Teuvo Kohonen. The learning algorithm independently generates classifiers, which it uses to divide the input patterns into (previously unknown) clusters. The goal is to achieve maximum homogeneity among patients within a cluster and maximum inhomogeneity among clusters. Core idea (topographic feature map): "Neighboring" input vectors (here, patient data) should belong to neighboring neurons in the map so that the density and distribution of the neurons correspond to the probability model of the training set.Advantages: Neighborhood relationships in the "confusing" input space can be read directly in the output layer. Applications: SOMs are used for clustering, visualizing complex relationships, prediction (evaluation), modeling, and data exploration. The focus of the application for the present research questions is clustering, visualization, and prediction. (Tools: e.g., Self Organizing Maps in R (R is a free programming language for statistical calculations and graphics. R is part of the GNU project, see also.) and Figure 1) 1.1) Formal description of the Kohonen network model (algorithm) ^ A SOM consists of two layers of neurons (input layer and output layer), see also Figure 2. ^ Each neuron in the input layer is connected to each neuron in the output layer (the neurons in the input layer are fully interconnected with those in the output layer). Each neuron in the input layer corresponds to a parameter of the data set. The number of input neurons is the dimension of the input layer. The output neurons are related to each other by a neighborhood function. ^ The strength of the connection is represented by a number (= weight) w[i][j] (w[i][j] is the weight that indicates the strength of the connection between the i-th input neuron and the j-th output neuron). The vector w[j] represents all weights w[i][j] (i=1…n, n is the number of parameters recorded for each patient) to the j-th output neuron.Input vectors and weight vectors are normalized (length = 1). ^ Initialize the weight vectors. Arbitrary values ​​generated by a random generator are specified as initial values ​​for the weights. ^ Each patient p defines an input vector Input through its values. p = (x p [1], x p [2],…, X p [n]) with the components Input p [i] = x p [i]. These input vectors are first normalized to 1. ^ For each patient p and each neuron j in the output layer, the Euclidean distance s [j] ||w[j] input || ^ w[ 2 p ^ ^ p ^ ^ j][i] ^ input p [i] ^i between the weight vector w[j] and the input vector Inputp is calculated. ^ The output neuron that has the smallest distance to the input vector Inputp is called the winner-takes-all neuron. The weight vector of the winner neuron is most similar to the input vector, ie, it has the "maximum excitation" under the input vector Inputp. If two or more output neurons have the same minimum distance, a neuron is randomly selected. ^ The winner neuron and its neighbors are given the "award" and are therefore allowed to represent the input. ^ This defines a function f(input p ) that assigns each vector input pof the input space (pattern space, feature space) is assigned a location α in a representational layer (map) f: inputp ^f(inputp) .= arg min(‖w[j] - inputp‖). The minimum is calculated over all weight vectors w[j]. The function arg returns the index of the winning neuron. ^ Determine the winning neuron for each patient (input pattern) according to the above rule. ^ Neighborhood function and weight adaptation: In the next step, the weights of the winning neuron and those of its surrounding neurons are adapted. The degree of proximity to the winning neuron plays a major role here. Let us assume that the winning neuron has the index ^. For an input[i] and an output neuron j, Dw[j][i] denotes the weight change within the framework of a learning rule. This is calculated as follows: Dw[j][i] = ^ ^t ^ ^(input[i] – w[i][j] )*NbdWt( ^, j) where ^ ^t ^ is the learning rate (η(t) with 0 < η(t) < 1) and NbdWt( ^, j) is the neighborhood weighting function.A neighborhood function calculates the degree of proximity of an output neuron to the winner neuron, which lies between 0 and 1. Convergence proofs against a statistically describable equilibrium state exist, for example, for ^ ^t ^ ^ ^ ^ ^ ^ ^t. ‐a with 1 <a≤1. ^ Nähere Gewichtsvektoren proportional zur Nachbarschaftsfunktion zueinan- der an Die Gewichtsvektoren w[j] aller Neuronen j gemäß der Nachbarschaftsfunk- tion NbdWt( ^, j) des Neurons ^ werden aktualisiert. Das neue Gewicht wneu[i][j] be- rechnet sich wie folgt: ^ w neu [j][i] := w[j][i] + Dw[j][i] ^ The neighborhood function can be one of the following functions: with d(α, j):= (α - j); s is a scalar. The width s = s(t) of the neighborhood function and the learning step size η(t) are reduced over time: the winning neuron and its neighbors are "activated" according to the function NbdWt(α, j). ^ Normalize weight vectors to length 1, present the next data point. For the various methods for color coding (heat maps) of maps, see the literature (e.g., http: / / citeseerx.ist.psu.edu / viewdoc / download?doi=10.1.1.100.500&rep=rep1&type=pdf, https: / / arxiv.org / pdf / 1306.3860.pdf or https: / / www.visualcinnamon.com / 2013 / 07 / self-organizing-maps-creating-hexagonal.html). 2) Classification algorithms: Three different classification algorithms are used to evaluate SOM models (class descriptions using facts and rules or decision trees): (1) Entropy-based learning methods (C5.0), (2) Exhausted Chain, and (3) CART. 2.1) Definition.Classification procedures are methods and criteria for classifying objects (here patients) into classes (here types and subtypes of healthy pre-diabetics or diabetics). From a training set of examples with known class membership, a classifier in the form of decision trees or, equivalently, in the form of facts and "if-then" rules is generated with the help of the classification algorithm. Classification differs from clustering (see also SOMs) in that in classification the classes are known a priori, whereas in clustering the classes must first be searched for. Decision trees are used for decision-making through a tree-like structure consisting of a root node (start node), nodes, edges, and leaves (end nodes) (Figure 2). Formally, a tree is a finite graph with the following properties: 1) There is exactly one node in which no edge ends (the "root").2) Exactly one edge ends in each node other than the root; the edges are directed. 3) Each node is reachable from the root by exactly one path. A decision tree is a tree: 1) Each node tests an attribute. 2) Each branch corresponds to an attribute value. 3) Each leaf (node ​​without outgoing edges) assigns a class. Figure 3 shows the formal components of a classification algorithm. Problem: In the first step, the classifier is optimized for the training data. It may deliver poorer results on the population of data (underfitting or overfitting: hypothesis class is too weak or too complex), see Figure 4. Possible overfitting can be reduced by pruning or boosting methods. Here, the expert selects the number of iteration steps required to improve group formation.Generally, the set of available examples is divided into two subsets (train-and-test). ^ Training set: for learning the classifier (model construction) ^ Test set: for evaluating the classifier. If this is not applicable because the set of objects with known class membership is small, so-called m-fold cross-validation is used instead of train-and-test. The following criteria are used as quality measures for classifiers: • Classification accuracy • Compactness of the model (e.g., size of a decision tree) • Interpretability of the model. • Efficiency • Robustness against noise and missing values ​​2.2) Construction of decision trees (See also - Quinlan, J. Ross (1986): Induction of decision trees. In: Machine Learning 1 (1), pp. 81–106. - Quinlan, J. Ross (1993): C4.5. Programs for machine learning. In: J. Ross Quinlan. San Mateo, Calif.: Morgan Kaufmann (The Morgan Kaufmann series in machine learning). - Quinlan, J.Ross (1996): Improved use of continuous attributes in C4.5. Journal of artificial intelligence research 4, pp. 77-90.) Basic algorithm loop: 1. Choose the "best" decision attribute A for the next node 2. For each value of A, create a new child node 3. Assign the training data to the child nodes 4. If the training data is classified correctly, then STOP. Otherwise, iterate over child nodes (→ 1.). Labels: training dataset T, number of training data |T|, classes ^^i: The dataset of all training data in class ^^i. | ^^i | is the number of elements in class ^^. i . The following applies: ∑ | ^^ i | = |T| (i=1,..k). Attribute ^^ = { ^^1, ^^2, … , ^^m}. The attribute A divides the data set T into m subsets T1, T2, … ,T m . |T ^^ | is the number of subset T i. Given training data set T and attributes ^^; Output: Information gain(T, ^^) of attributes ^^ for the training data set T 3) Algorithm (calculating the information gain using the entropy using ID3 as an example) • The empirical entropy for a set T of training objects with the classes Ci (i=1,…,k) is defined as ^ log p i with p i := | ^^ i | / |T| • The attribute A has the partitioning T1, T2, …, T m The empirically determined entropy G(T,A) for a set T and an attribute A is defined as • The information gain Gain(T, A) by attribute A with respect to T is defined as Input: Training dataset T with the classes ^^ (i=1, Attributes ^^ and ε ; Output: Decision tree E; Algorithm: 1. Create a node K; 2. If all sample data in T have an identical class ^^ jor the number of data is smaller than threshold ε, then the only node with class ^^j is returned to node K as a leaf; 3. If ^^ = ∅, then the only node with the most frequent class in T is assigned to node K as a leaf; 4. Calculate the information gain Gain of ^^ in T and determine the best attribute ^^ ^^ with maximum information gain using 5. Denote the node K with ^^ ^^ 6. Calculate for all attribute values ​​A gj from A g the subset T gi all examples from the training dataset with A gj7. If Tgi = ∅, then a leaf with the most frequent class is added to node K, otherwise 8. Recursion of the branch of ^^ ^^. Note: For a random event y, which occurs with probability P(y), the following applies: Information content: h(y) ≡ −log2(P(y)) The entropy is the mean value of the information content of the random variable y H[y]≡∑y P(y)h(y) = −∑y P(y)log2(P(y)) Classification algorithms used ID3 ^ Discrete attributes, no missing attributes ^ Information gain as a quality measure. C4.5 / C5.0 ^ Extension of ID3. ^ Information gain ratio as a quality measure. Missing attributes ^ Numerical and real-valued attributes ^ Pruning of the decision tree The Information GainRatio is defined as^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^^ ^ ^^, ^^^ ≔ Gain^T, A^SplitInformation^T, A^ CART (Classification and Regression Trees). See also Hastie, T., Tibshirani, R., Friedman, J.H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer Verlag. Procedure analogous to ID3 or C5.0. The information measure is defined by the Gini index. The Gini index is minimized (instead of maximizing the Gini gain). Gini gain k Gini ( T , A ) ^ ^ T i ^Gini( T i ) i ^ 1 T With P i is the relative frequency of class C iin T CHAID (Chi-square Automatic Interaction Detectors) See also Sonquist, JA and Morgan, JN (1964): The Detection of Interaction Effects. Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor. CHAID is another algorithm for constructing decision trees. The differences to C5.0 or CART are that the CHAID algorithm uses the chi-square test of independence to select attributes and that the CHAID algorithm stops the tree from growing before it becomes too large. The tree is therefore not allowed to grow arbitrarily and then pruned again using a pruning method. 4) Radial Basis Function Networks (RBF Networks) See also Zell, A.: Simulation of Neural Networks. Oldenbourg 1994 RBF networks are used to further validate the models and calculate the importance of biomarkers for the various classification models. RBF networks create predictive models.They are particularly suitable for the approximation of functions. The RBF network consists of an input layer with n neurons, a hidden layer with k neurons, and an output layer with m neurons. An n-dimensional pattern is thereby mapped into an m-dimensional output space. The input layer is a pure forwarding layer. Each neuron distributes its value to all hidden neurons. In the hidden layer, the distance between the input and the center c is calculated in each neuron using a Euclidean norm. Radial basis functions are used as the network input and activation function (see Figure 5). The activation function of each hidden neuron is a so-called radial function, i.e., a monotonically decreasing function f : IR0. + → [0, 1] with f(0) = 1 and ^ li → m ^^^^ ^^^ ൌ 0m The input vectors are normalized (subtracting the mean and dividing by the range (x-min) / (max-min); normalized values ​​lie between 0 and 1). The softmax function σ is used as the activation function as a normalized radial basis function. Softmax σ maps a k-dimensional vector z to a k-dimensional vector σ(z). The number of neurons in the hidden layer is determined by the Bayesian Information Criterion (BIC) (see Schwarz, Gideon E. (1978), "Estimating the dimension of a model", Annals of Statistics, 6 (2): 461–464, MR 468014, doi:10.1214 / aos / 1176344136). The best number of hidden units is the one that results in the smallest BIC based on the training data. For the output layer, we used the identity function as the activation function. The output units are therefore simply weighted sums of the hidden units. The output of the network is therefore a linear combination of the radial basis functions of the inputs and the weights. Network Performance Network performance checks how "good" the network is. A range of outputs are provided for this purpose. ^ Model summary. Results including error, relative error or percentage of incorrect predictions and training time. ^ Classification results.A classification table is provided for each categorical dependent variable. ^ ROC curves. The ROC curves (Receiver Operating Characteristic curves) indicate the sensitivity and specificity for each possible cutpoint of the input variable. The area under the curve (AUC) is a measure of the quality of the classification. ^ Cumulative gain diagrams. A use according to the invention is preferred, wherein the use occurs concomitantly with or in preparation for a bariatric procedure. A bariatric procedure within the meaning of this text is any surgical intervention used as a measure against obesity (bariatric surgery). This includes in particular the proximal Roux-en-Y gastric bypass and sleeve gastrectomy, but also biliopancreatic diversion with or without duodenal switch, gastric band implantation, omega loop gastric bypass, and other procedures. It has surprisingly turned out that the forecast andDiagnostic results of the use according to the invention are very precise in connection with a bariatric procedure. Preference is given to a use according to the invention, wherein a distinction is made, in particular, during diagnosis, between NAFLD and NASH. According to the current state of the art, a reliable differentiation between non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) is only possible with particular reliability by means of a liver biopsy (see also above). Surprisingly, it has now been found that the use according to the invention is capable of providing a corresponding differentiation for the respective test subject with a high degree of certainty in samples, particularly in fat samples. Preference is given to a use according to the invention wherein a prognosis of a deterioration in the condition of the liver, in particular of the onset of new or the progression of existing liver fibrosis, is made.It has been found that with the use according to the invention, a corresponding prognosis is reliably possible. This applies in particular in combination with a bariatric procedure. A use according to the invention is preferred, wherein the prognosis of a deterioration in the liver condition is related to the probability of type 2 diabetes remission, in particular after a bariatric surgery. With this preferred use according to the invention, not only is a prognosis of a deterioration in the liver condition possible, but - as has surprisingly been shown - a prediction of type 2 diabetes remission can also be achieved with good results. This also applies in particular in combination with bariatric procedures on the patient.A preferred use according to the invention is for prognosis, wherein the sample examined is assigned to one of the three following risk clusters: C1: Increased probability of type 2 diabetes remission and reduced risk of liver fibrosis; C2: Increased probability of type 2 diabetes remission and increased risk of liver fibrosis; C3: Reduced probability of type 2 diabetes remission and reduced risk of liver fibrosis. “Risk groups” within the meaning of this text are those groups that can be separated from one another by suitable distinguishing features and each have a common increased or non-increased risk with regard to the development or presence of a disease, in particular NAFLD, NASH, liver fibrosis, liver cirrhosis and hepatocellular carcinoma. In addition, risk groups can also differ from one another due to further physiological differences, which mayhas therapeutic or prophylactic relevance. It is surprising that the use according to the invention enables correspondingly complex predictions regarding both the prognosis of liver fibrosis and type 2 diabetes remission. Generally, to determine the relative expression values ​​(see above), it is necessary to use a calibrator value for the respective gene or gene expression. In case of doubt, the expert can select the following calibrator values ​​for the delta-Ct for the following genes: Table 1. Taking into account the markers listed in the table below, the mean value entered in the table results for the respective clusters. Due to fluctuations within a patient group, the respective values ​​are, of course, to be considered as values ​​with a margin of + / - 10%. However, the distribution pattern of the values ​​in the three clusters (highest mean HMGA2 expression in cluster 1, highest mean ADIPOQ and PPARG expression in cluster 2, highest mean HbA1c value in cluster 3) is independent of this. Table 2: In bold, those mean values ​​that are particularly relevant for describing the clusters are shown. For this purpose, reference is also generally made to the examples. A preferred use according to the invention is one in which the liver disease is selected from the group consisting of NAFLD, NASH, liver fibrosis, liver cirrhosis, and hepatocellular carcinoma. The use according to the invention has proven particularly suitable for these types of disease.Part of the invention is also a method for the prognosis and / or diagnosis of liver disease, comprising the steps: a) in vitro providing a sample from a test subject, b) determining the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression is linearly statistically correlated with that of the HMGA2 gene, and c) relating the value determined in step b) to one or more gene expression level values, as defined in more detail above and / or to one or more factors as defined in more detail above, and d) classifying the test subject, taking into account the result of step c), in the case of diagnosis into a group with a disease profile or in the case of prognosis into a group with a risk profile. In preferred methods according to the invention, the steps described above for the use according to the invention can also be used (in case of doubt, likewise preferred).The method according to the invention makes it possible to predict or diagnose liver diseases. Preferably, it is also possible to make a statement regarding type 2 diabetes remission (see above), this is particularly possible in combination with bariatric surgery. The advantages described above for the use according to the invention naturally also apply to the method according to the invention in a corresponding embodiment. The invention is further explained below using examples: Example 1 Risk prediction of advanced liver fibrosis using gene expression analyses of HMGA2, PPAR-gamma, ADIPOQ and IL-6 in subcutaneous fatty tissue samples Materials and Methods Tissue samples The human subcutaneous abdominal fatty tissue was removed during operations and stored in liquid nitrogen after the operation. The requirements of the Declaration of Helsinki were met for all human samples used.Written informed consent for the use of the tissue samples was provided by the patients (n = 106). RNA isolation Total RNA was isolated using the RNeasy Lipid Tissue Mini Kit (QIAGEN, Hilden, Germany) in a QIAcube (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. Tissue samples (up to 100 mg) in 1 ml QIAzol Lysis Reagent were homogenized in a gentleMACS Octo Dissociator (Miltenyi Biotec, Bergisch Gladbach, Germany), centrifuged down, and transferred to a new 2 ml reaction tube. The homogenate was then incubated at room temperature for 5 min. This was followed by the addition of 200 µl of chloroform, which was mixed with the sample by vigorous manual shaking for 15 sec. The sample was incubated again for 2 min at room temperature and centrifuged at 12,000 x g for 15 min at 4 °C.The upper aqueous phase was then transferred to a new 2 ml cup, and total RNA was isolated using a Qiagen Rneasy Mini Spin column (QIAGEN, Hilden, Germany) in a QIAcube according to the manufacturer's instructions. cDNA Synthesis For cDNA synthesis, ≤ 250 ng of RNA was transcribed into cDNA using 200 U M-MLV reverse transcriptase, Rnase Out (Invitrogen, Darmstadt, Germany), and 150 ng of random primer (Invitrogen, Darmstadt, Germany) according to the manufacturer's instructions. The RNA was denatured at 65 °C for 5 min and then stored on ice for at least 1 min. After addition of the enzyme, the mix was incubated for 10 min at 25 °C to anneal the random primer to the RNA. Subsequent reverse transcription was performed at 37 °C for 50 min, followed by inactivation of the reverse transcriptase at 70 °C for 15 min.cDNA Pre-Amplification: 5 µl of cDNA was pre-amplified using PerfeCTa PreAmp SuperMix (Quantabio, Beverly, Massachusetts, USA) using HMGA2 and HPRT (hypoxanthine phosphoribosyltransferase 1)-specific primers according to the manufacturer's instructions. The cDNA pre-amplification was performed according to the following temperature profile: 95°C for 2 min followed by 14 cycles at 95°C for 10 sec and at 60°C for 3 min. Quantitative Real-Time PCR (qRT-PCR) Relative quantification of gene expression was performed using real-time PCR on the Applied Biosystems 7300 Real-Time PCR System. Commercially available gene expression assays (Life Technologies, Carlsbad, CA, USA) were used to quantify mRNA levels of HMGA2 (Assay ID Hs00171569_m1) and PPAR-gamma (Assay ID Hs01115513_m1). As an endogenous control, as described by Klemke et al.(Klemke M, Meyer A, Hashemi Nezhad M, Belge G, Bartnitzke S, Bullerdiek J (2010) Loss of let-7 binding sites resulting from truncations of the 3' untranslated region of HMGA2 mRNA in uterine leiomyomas. Cancer Genet Cytogenet 196:119-123) was used. All measured samples were determined in triplicate. Quantification of gene expression was performed in 96-well plates using the preamplified cDNA to be investigated, the respective gene-specific assay, and the FastStart Universal Probe Master (Rox) (Roche, Mannheim, Germany). The temperature profile of the real-time PCR followed the manufacturer's instructions: The template was denatured at 95 °C for 10 minutes. This was followed by amplification in 50 cycles, beginning with denaturation for 15 seconds at 95 °C and then a combination of annealing / elongation for 60 seconds at 60 °C. The resulting data were analyzed using the comparative delta-Ct method (ΔΔCT method).[(Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2. –ΔΔC(T)Method. Methods 25: 402-408)] Results There is growing clinical and mechanistic evidence reporting the beneficial effects of bariatric surgery on metabolism and weight loss in improving non-alcoholic fatty liver disease (NAFLD) in obese patients. Bariatric surgery is associated with significant improvement in both histological and biochemical markers of NAFLD. Studies related to bariatric surgery and liver disease show a significant reduction in the incidence of several histological features of NAFLD, including steatosis, fibrosis, hepatocyte ballooning, and lobular inflammation. Bariatric surgery is also associated with reductions in liver enzymes, with statistically significant reductions in ALT, AST, and gamma-GT [Bower G, et al.: Bariatric Surgery and Non-Alcoholic Fatty Liver Disease: a Systematic Review of Liver Biochemistry and Histology; Obes Surg. 2015 Dec;25(12):2280-9. doi: 10.1007 / s11695-015-1691-x]. In contrast, studies also show that excessively rapid weight loss due to bariatric surgery can lead to a significant worsening of existing steatohepatitis [Andersen T, Gluud C, Franzmann MB, Christoffersen P. Hepatic effects of dietary weight loss in morbidly obese subjects. J Hepatol 1991; 12: 224–9.;Luyckx FH, Scheen AJ, Desaive C, Dewe W, Gielen JE, Lefebvre PJ. Effects of gastroplasty on body weight and related biological abnormalities in morbid obesity. Diabetes Metab 1998; 24: 355–61.]. Currently, there is no method that can identify individuals who experience worsening of their liver disease after bariatric surgery.A study by the inventors examined a cohort of patients who underwent bariatric surgery to determine the impact of the procedure on remission of their type 2 diabetes (T2D) and their liver function after one year. For this purpose, the parameters gene expression of HMGA2, PPAR-gamma, ADIPOQ, and IL-6 in subcutaneous abdominal fat, as well as the HbA1c value at the time of surgery, were statistically analyzed using self-organizing maps (SOMs). This study found that, using these parameters and the statistical analysis, patient subgroups could be identified who did not experience remission (C3) as well as those who experienced T2D remission as a result of this bariatric procedure. Surprisingly, two different subgroups (C1 and C2) were identified, each of which experienced T2D remission.With regard to an improvement in liver values ​​and NAFLD after one year, especially liver fibrosis, it was shown that patients in the T2D remission cluster C2 are more likely to experience a worsening of their liver fibrosis than the other two clusters C1 and C3. The FIB4 score can be used to estimate the risk of advanced liver fibrosis. The FIB4 score is calculated using the following formula: FIB4 score = (age x AST) / (platelet count x (√( ALT )). For low values ​​(< 1.3 in patients under 65 years of age, < 2.0 in patients 65 years of age and older), there is no increased risk; for values ​​> 3.25, the risk of advanced fibrosis is increased. For values ​​in between, further tests are required to estimate the risk.The FIB4 score was calculated for 106 patients, and then classified into three categories: "low risk for advanced fibrosis," "advanced fibrosis unclear," and "high risk for advanced fibrosis." All 106 patients underwent bariatric surgery. Twelve months after these procedures, the FIB4 scores were recalculated. This showed that eight patients were assigned to a category with a more favorable risk prediction than at the time of the procedure. 93 of 106 patients initially fell into the low-risk category. One year after surgery, 45 patients (equivalent to 48.4%) with an initial low risk had to be assigned to a category with a less favorable risk prediction for advanced liver fibrosis based on the FIB4 score. Table 3: Cross-tabulation of initial FIB4 categories versus FIB4 categories after 12 months. The study investigated whether there is a correlation between the parameters initial BMI (at the time of surgery) and the expression of the genes HMGA2, PPARG, ADIPOQ, and IL-6 (each determined at the time of surgery) and the deterioration of the FIB4 score after 12 months. The FIB4 score determined after 12 months appears to be independent or relatively unrelated to the initial BMI or the expression of the genes PPARG, ADIPOQ, and IL-6. In contrast, it was shown that a deterioration of the FIB4 score after 12 months occurs predominantly in patients who initially had lower HMGA2 expression. In addition to the FIB4 score, it therefore appears appropriate to determine HMGA2 expression in subcutaneous fatty tissue, particularly during bariatric surgery.With their help, patients whose FIB4 score will worsen within one year and who are thus at increased risk of progressive liver fibrosis can be identified at the time of surgery. If such high-risk patients are identified based on reduced HMGA2 expression, they can be monitored more closely, e.g., using elastography, in order to detect the potentially severe form of liver fibrosis at an early stage. For this purpose, a cluster analysis of the 106 patients was performed using Kohonen's self-organizing maps (SOMs). The parameters considered were the initial HbA1c value at the time of surgery and the expression levels of the genes HMGA2, PPARG, ADIPOQ, and IL-6. This approach resulted in three clusters of patients characterized by different characteristics. Figure 5a shows the summary results of the SOM analysis.This results in three distinct clusters, C1 to C3. Figures 5b to 5f show the expression of IL-6 and HMGA2, initial HbA1c, and ADIPOQ and PPARG genes in the three clusters. The table shows the mean values ​​determined for each cluster. Cluster C1 is characterized by increased HMGA2 expression, while in cluster C2 the PPARG and ADIPOQ genes are more highly expressed. Cluster C3 is characterized by high initial HbA1c values. To assign patients to the three clusters, the following eight rules are calculated using the C5.0 algorithm and are required for patient classification: Table 4: Rules for assigning patients to one of three clusters. The clusters differ with regard to type 2 diabetes remission. While cluster C3 includes those patients who did not experience remission after bariatric surgery, patients in both clusters 1 and 2 experienced remission one year after surgery. Table 5: Classification of patients with T2D remission after one year It is evident that the methods used, based on gene expression levels, allow for very high prediction accuracy, even with regard to T2D remission: In the entire patient collective of 106 individuals, the prediction derived from the SOM model deviated in only one case, in that in group C3, T2D remission occurred contrary to expectations. In particular, the positive prognosis of an onset of T2D remission represents an important reason for deciding on surgery in cases of doubt. The corresponding SOM pattern is also shown in Figure 6. Furthermore, significant differences between the clusters were observed with regard to the risk of advanced liver fibrosis one year post-operatively. This risk is lower in both cluster C1 (with remission) and cluster C3 (no remission), meaning that the majority of patients in these clusters (62.7%) have a low risk.In contrast, the second cluster with remission, C2, is assigned more patients with an increased risk of advanced liver fibrosis (66.7%). Figure 7 shows the result of the SOM analysis with regard to FIB4 development after twelve months. In addition, Figure 8 shows a surface plot of the FIB4 score after twelve months as a function of the relative expression level of HMGA2 and the initial FIB4 score. This shows the significant influence of HMGA2 on the resulting clusters. Example 2: The assessment and prognosis of severe liver damage based on the expression levels of the HMGA2, PPARG, ADIPOQ, and IL-6 genes. Materials and Methods: Sample preparation: The tissue samples were obtained as described in Example 1. The subsequent steps of sample preparation, i.e. RNA isolation, cDNA synthesis, pre-amplification of the cDNA and quantitative real-time PCR, were also carried out as described in Example 1.Statistical analysis See Example 1 (deviations mentioned here).

[0002] Results The De-Ritis quotient is used to estimate the severity of liver damage and is calculated as follows: De-Ritis quotient = AST / ALT. Values ​​< 1 indicate less severe liver damage, while values ​​> 1 indicate more severe liver damage (e.g., chronic hepatitis, liver cirrhosis). The De-Ritis quotient determined one year after bariatric surgery depends significantly on the value determined at the time of surgery. However, statistical analysis showed that not only the initial De-Ritis quotient, but also the initial BMI, age and gender, as well as the expression levels of the genes HMGA2, PPARG, ADIPOQ, and IL-6, affect the De-Ritis quotient determined one year after bariatric surgery.The following characteristics were included in the statistical analysis: Biomarkers: - RQ HMGA2 (77.4%), - RQ ADIPOQ (49.1%), - RQ IL-6 (11.3%), - RQ PPAR-gamma (2.8%) and as baseline characteristics - gender (46.4%) - initial De-Ritis quotient (70.8%) - age (60.34%) - initial BMI (50.0%) The values ​​in parentheses indicate the influence of each marker on the evaluation result. As a result, 78 patients were classified in the class with lesser liver damage after twelve months (Cluster A), while 28 patients were classified in Cluster B with greater expected liver damage (each after twelve months). The evaluation after twelve months showed that only two patients from Cluster A were incorrectly classified and only four from Class B. Thus, the system used can provide a relatively good forecast for developments within a period of twelve months.It is noteworthy that the relative expression level of HMGA2 had the greatest influence on group allocation. The decision tree for the classification is presented below in Table 6.

[0003] Table 6: Prediction of the severity of liver damage after 12 months: Differentiating NAFLD from NASH based on the expression levels of the genes HMGA2, PPARG, ADIPOQ, and IL-6 in subcutaneous adipose tissue Materials and Methods Sample preparation The tissue samples were obtained as described in Example 1. The subsequent sample preparation steps, i.e. RNA isolation, cDNA synthesis, cDNA pre-amplification, and quantitative real-time PCR, were also performed as described in Example 1. Statistical analysis See Example 1 (deviations are indicated here). Results Liver biopsy is still the gold standard for diagnosing NAFLD, because NASH can only be formally diagnosed histologically. However, the biopsy is invasive and carries the risk of potentially life-threatening complications such as bleeding.A dataset of 52 cases was also used to investigate whether the stages of nonalcoholic steatohepatitis (NASH) and nonalcoholic fatty liver disease (NAFLD) can be differentiated. For this purpose, the parameters cluster assignment, FIB4 score, and expression levels of the genes HMGA2, PPARG, ADIPOQ, and IL-6 were used, and a set of rules for classifying the patients was developed using the C5.0 algorithm. The following eight rules are required to assign patients to the NASH or NAFLD group: Table 7: Rules for differentiating patients with NASH and NAFLD. Using these rules, the stage of liver disease was correctly determined in 49 of 52 cases, corresponding to an error rate of 5.8%. One case of NAFLD was incorrectly identified as NASH, and two cases of NASH were incorrectly classified as NAFLD. An important tool for assessing fatty liver disease to date is the NAFLD activity score (NAS). Because it is based on histological criteria, a liver biopsy must be performed, which carries certain risks (e.g., bleeding). This invasive procedure can be replaced with the system described here, which safely takes a sample from the subcutaneous fat.

Claims

1. Use of the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression is statistically correlated with that of the HMGA2 gene in the prognosis and / or diagnosis of fatty liver disease in a subject.

2. Use according to claim 1, wherein the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression is statistically correlated with that of the HMGA2 gene is related to the relative value of the gene expression level of the ADIPOQ gene and / or a gene whose expression is statistically correlated with that of the ADIPOQ gene.

3. Use according to claim 1 or 2, further wherein the relative value (i) of the gene expression level of the HMGA2 gene or (ii) of the HMGA2 gene and the ADIPOQ gene is related to the relative value of one or preferably both gene expression levels of the genes selected from the group consisting of PPARgamma and IL-6. 4.Use according to any one of the preceding claims, wherein one or more factors selected from the group consisting of BMI, sex, DeRitis quotient, age, FIB4 score, HbA1c value, and serum concentration of one or more enzymes selected from the group consisting of alanine aminotransferase, aspartate aminotransferase, glutamate dehydrogenase, gamma-glutamyltransferase, and alkaline phosphatase are related to the analysis.

5. Use according to any one of the preceding claims, wherein the relative value of the gene expression level or values ​​of the gene expression levels are determined in vitro from an adipose tissue sample.

6. Use according to claim 5, wherein the sample was obtained by puncturing abdominal adipose tissue, preferably subcutaneous abdominal adipose tissue.

7. Use according to any one of the preceding claims, wherein the determination of the gene expression level or levels is performed at the mRNA level.

8. Use according to any one of the preceding claims, wherein the value or values ​​for the relative gene expression levels and optionally one or more factors as specified in claim 4 are related using the Kohonen self-organizing map multivariate model.

9. Use according to any one of the preceding claims, wherein the use is performed concurrently with or in preparation for bariatric surgery.

10. Use according to any one of the preceding claims, wherein a distinction is made between NAFLD and NASH.

11. Use according to any one of claims 1 to 9, wherein a prognosis of deterioration of liver condition, in particular the onset of new or the progression of existing liver fibrosis, is performed.

12. Use according to claim 11, wherein the prognosis of deterioration of liver condition is related to the probability of type 2 diabetes remission. 13.Use according to claim 12, wherein the sample under investigation is assigned to one of the following three risk clusters: C1: Increased probability of type 2 diabetes remission and decreased risk of liver fibrosis; C2: Increased probability of type 2 diabetes remission and increased risk of liver fibrosis; C3: Decreased probability of type 2 diabetes remission and decreased risk of liver fibrosis.

14. Use according to any one of the preceding claims, wherein the liver disease is selected from the group consisting of NAFLD, NASH, liver fibrosis, liver cirrhosis, and hepatocellular carcinoma.

15. Method for prognosticating and / or diagnosing liver disease, comprising the steps of: a) In vitro, providing a sample from a subject, b) determining the relative value of the gene expression level of the HMGA2 gene and / or a gene whose expression is statistically correlated with that of the HMGA2 gene, and c) relating the value determined in step b) to one or more gene expression level values ​​as further defined in claim 2 or 3 and / or to one or more factors as further defined in claim 4, and d) classifying the subject, taking into account the result of step c), in the case of diagnosis into a group with a disease profile or in the case of prognosis into a group with a risk profile.

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