Diagnosis of metabolic changes of the liver by way of metabolic analysis of erythrocytes by mass spectrometry
Patent Information
- Application Number
- EP2023813379
- Authority / Receiving Office
- EP · EP
- Patent Type
- Applications
- Current Assignee / Owner
- Priority Date
- 2022-11-30
- Filing Date
- 2023-11-27
- Publication Date
- 2025-10-08
AI Technical Summary
Current methods for diagnosing liver diseases are limited by their complexity, imprecision, and inability to comprehensively assess metabolic changes, often requiring complex sample preparation and providing incomplete information on liver function and dysfunction.
A method using mass spectrometry to analyze key metabolites in erythrocytes, specifically phospholipids and sphingolipids, from biological samples like blood or urine to detect changes in lipid profiles, enabling early diagnosis and treatment of liver diseases.
This approach allows for precise and comprehensive assessment of liver function and metabolic changes, facilitating early diagnosis and personalized treatment strategies, and can differentiate between various types of liver damage.
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Figure 1.1
Abstract
Description
[0001] University Hospital Jena Dean's Office Medical Faculty Third-party funding applications / patent procedures Kastanienstraße 1 07747 Jena
[0002] Diagnosis of liver metabolic changes using metabolic analysis of erythrocytes by mass spectrometry
[0003] Description
[0004] The invention relates to methods for analyzing a biological sample for changes in signatures of metabolites in a liver disease, and to the use of the results of the method in personalized medicine.
[0005] Background of the invention
[0006] The invention lies in the field of individualized medicine, which plays a particularly important role in complex disease processes. The concept of "individualized" or "personalized" care, which implies patient stratification based on a better assessment of the complex disease process, is challenging but promising for critically ill patients. More comprehensive metabolic monitoring using mass spectrometry to assess the "metabolome," or "metabolomics," is currently entering clinical routine (Kiehntopf et al., 2013). This technique can be performed in a "non-targeted" approach or to monitor specific groups of analytes. The metabolome, as a complex reaction network, summarizes all the characteristic metabolic properties of a cell, tissue, or organism.The term "metabolome" was coined as "the sum of all metabolic products (metabolites)" in analogy to the terms genome, transcriptome, and proteome, and was named accordingly. The word is derived from metabolism. The term "metabonomic" is occasionally used for "metabolism," particularly in the context of drug toxicity assessment.
[0007] The metabolome includes:
[0008] • the flow rates (= turnover rates), metabolite levels and enzyme activities of the individual metabolic pathways,
[0009] • the interactions between the different metabolic pathways and
[0010] • the compartmentalization of the various metabolic pathways within the cells.
[0011] Another aspect is the influence of the nutrient supply as well as the effect of active substances on the metabolism and the various functions of the cells, such as cell proliferation, differentiation and apoptosis.
[0012] The study of the metabolome is called metabolomics (or metabonomics). This encompasses the interaction of the metabolites it contains, their identification, and quantification (cf. proteome and genome). The main analytical methods used in metabolomics are GC / MS and LC / MS, as well as NMR spectroscopy and ion mobility spectrometers. These methods can be divided into separation techniques, ionization techniques, and detection techniques. Because metabolites can vary greatly in their chemical composition and structure, it is often not sufficient to use just one analytical method to fully elucidate an organism's metabolome. Furthermore, the exact number of metabolites that exist remains unknown. The most common sample types are body fluids such as plasma or serum, but also urine, cerebrospinal fluid, synovial fluid, sputum, or lavage fluids.In addition, tissue shornogenates or cells or supernatants from cell cultures can be considered.
[0013] In the field of liver diseases, analytes such as unconjugated and glycine / taurine-conjugated primary bile acids are measured using mass spectrometry, as in the study by Vanwijngarden et al. While bilirubin has traditionally been used exclusively to monitor excretory liver function in the intensive care unit, e.g., in the most widely used scoring system for multiple organ dysfunction, the sepsis-related (sometimes also sequential) organ failure assessment (SOFA) score (Vincent et al., 1996), recent findings suggest that bile acids may indicate liver dysfunction with higher sensitivity and specificity (Recknagel et al., 2012) or at least may be elevated largely independently of bilirubin (Vanwijngaerden et al., 2014).In any case, even considering the inaccuracy of current clinical methods for assessing even the smallest changes, these modest elevations in bilirubin can be associated with a pronounced impairment of phase I and II metabolism, which is associated with a negative prognosis in these patients (Kruger et al., 2009). In addition to the analysis of plasma and serum metabolites using mass spectrometry, numerous different methods are currently used in clinical functional diagnostics to diagnose metabolic disorders. These include biochemical assays (so-called clinical chemistry), breath gas analysis, and pulse densitometry. Metabolic disorders of the liver are investigated using endogenous clearance assays.Biochemical measurements of enzyme activities (transaminases, alkaline phosphatase) are used to assess liver damage and combined with liver function-associated metabolites (cholesterol, bilirubin, albumin) to examine liver function.
[0014] Furthermore, exogenous clearance assays are used clinically. These include, among others: the indocyanine green test (fluorescence-based measurement of excretory function); the galactose tolerance test (injection of galactose and measurement of the portion excreted via the kidney); the C 14 -Aminopyrine or 14 C-galactose breath test; and caffeine clearance.
[0015] These assays are supported in the experimental area with imaging methods based on matrix-assisted laser desorption ionization (MALDI) imaging.
[0016] However, the conventionally used methods have numerous disadvantages. Biochemical assays often require complex sample preparation and pre-analytical testing. The transport of these often sensitive biological samples is critical. Breath gas analysis is very complex. In addition, there are numerous clinical disadvantages. Biochemical assays are often imprecise, i.e., they have low specificity, and do not provide precise information about the degree of dysfunction. Often, the assessment is based on liver damage parameters, which only partially indicate the loss of function and the type of loss of function. Differentiation of liver damage is not possible with these assays. Breath gas analysis is generally time-consuming and offers little added value. Only individual analytes are examined. As with fluorescein angiography (indocyanine clearance test), its significance is limited to determining liver function.The nature of the dysfunction can only be assessed indirectly.
[0017] When imaging metabolites (research) using MALDI, sample preparation is very complex: Special coated (matrix) slides are used. Different coatings must be used for different metabolite groups. Certain metabolite groups cannot be measured simultaneously in a sample. Imaging typically destroys the sample, making subsequent analysis impossible. The maximum achievable resolution is relatively low (micrometer scale).
[0018] There is therefore a need to provide improved methods for a more comprehensive assessment of metabolism and the metabolome. In the field of liver disease, such improved methods are likely to challenge the preconceived notion of liver dysfunction as a rather late and rare event in the ICU and could be suitable tools to better monitor hepatobiliary function and personalize metabolic needs and nutritional support in critically ill patients (Bauer & Kiehntopf, 2014).
[0019] WO 2022 / 167521 A1 relates to a method for analyzing a biological sample for changes in signatures of bowl metabolites in a disease, comprising
[0020] (a) the determination of desired measurement areas in the biological sample using an image of the biological sample generated by optical methods,
[0021] (b) an image-based screening of the desired measurement areas determined in step (a) using at least one spectroscopic method, comprising o the identification and quantification of key metabolites of the metabolic disease, o the detection of changes in the key metabolites of the metabolic disease, and
[0022] (c) the diagnosis of a disease or the determination of the distribution of key metabolites in the biological sample.
[0023] The method according to WO 2022 / 167521 A1 is particularly suitable for the diagnosis of metabolic and proliferative diseases, but is technically complex and corresponding machines are not yet fully established and certified for routine clinical use.
[0024] Description of the invention
[0025] The object of the invention was to provide methods for a more comprehensive assessment of metabolism and the metabolome that do not have the disadvantages of the state of the art and can be established in clinically established infrastructures using the measurement method of mass spectrometry.
[0026] To achieve this object, the invention provides a method for analyzing a biological sample for changes in signatures of bowl metabolites in a liver disease according to claim 1.
[0027] According to the invention, a “biological sample” is understood to mean any type of biological material in which erythrocytes are present or from which erythrocytes can be isolated.
[0028] In a preferred embodiment, the amount of key metabolites is detected in a body fluid sample derived from a mammal, most preferably a human. The term "body fluid" refers to all fluids present in the human body in which red blood cells are present or from which red blood cells can be isolated. Preferably, body fluids within the meaning of the invention are selected from blood and urine. A blood sample is particularly preferred. The blood sample can be treated prior to use, e.g., with an anticoagulant such as EDTA.
[0029] The biological sample can also be a tissue sample or a biopsy containing traces of blood and thus red blood cells. The biological sample can also be a sample from a cell culture of red blood cells.
[0030] In a particularly preferred embodiment, the biological sample is a sample selected from the group comprising blood, urine, a tissue sample, a biopsy, and a cell culture, wherein the biological sample contains erythrocytes.
[0031] In one embodiment of the invention, the method for analyzing biological samples for changes in signatures of key metabolites in a disease includes the step of taking the biological sample from a subject. However, it is particularly preferred if the method according to the invention is performed on a biological sample that has previously been taken from a subject.
[0032] The term "subject" refers to a mammal that suffers from, or is suspected of suffering from, a disease, such as a metabolic disorder or cancer. Preferably, the term "subject" refers to a human.
[0033] The method according to the invention is based in particular on using erythrocytes as a cellular compartment to uncover potential differences in lipid profile and enable early diagnosis and treatment of liver diseases. The lipids present in erythrocytes are the key metabolites analyzed using the method according to the invention.
[0034] Erythrocytes represent the largest cellular fraction of blood, with 4–5 million erythrocytes per microliter of blood. Morphologically, erythrocytes are biconcave, circular cells with a diameter of approximately 7.5 μm. Hemoglobin is the molecular equivalent that causes the red color. It is the iron-containing metalloprotein for oxygen transport, enabling the primary function of erythrocytes. Upon passing through the pulmonary capillaries, red blood cells bind oxygen to hemoglobin, forming oxyhemoglobin for peripheral transport. (Kuhn et al., 2917) However, erythrocytes are also involved in other physiological processes, such as regulating blood vessel tone caused by shear stress. (Sun et al., 2021) The release of adenosine triphosphate (ATP) or S-nitrosothiols can directly relax the vessel walls and thus improve microvascular transfusion. (Barvitenko et al., 2005, Kuhn et al.(2017) Interestingly, red blood cells lack a nucleus, ribosomes, and an endoplasmic reticulum. (Smith, JE, 1987) Erythrocytes are incapable of protein synthesis or aerobic cellular respiration. Anaerobic glycolysis therefore remains the only viable strategy for ATP generation. Another implication from erythrocyte physiology is that proteomic alterations cannot be as adaptive to microenvironmental changes due to the inadequate protein synthesis machinery, making erythrocytes an interesting target for detecting molecular alterations.
[0035] The surrounding cell membrane consists primarily of a typical lipid bilayer. Cholesterol and phospholipids are the essential elements of the inner and outer layers of the membrane. Comparing the two structures reveals differences in lipid distribution. While phosphatidylcholines (PC) and sphingomyelins (SM) are more common on the surface, phosphatidylethanolamines (PE) and phosphatidylserines (PS) predominate in the intracellular layer (see also Figure 1). This defined lipid distribution is crucial for cell integrity and is maintained by the enzymes flippase and scramblase. (Mohandas et al., 2008) A network of more than 50 membrane proteins enables the cell to fulfill the diverse functions mentioned above.
[0036] Today, the diagnosis of red blood cells primarily focuses on quantitative and morphological patterns. The characterization of red blood cell properties is based primarily on the number and volumetric characteristics in general. The microscopic appearance of red blood cells in a blood smear can reveal abstract formations such as acanthocytes, fragmentocytes, or spherocytes, which can facilitate the diagnostic process in many cases. (Düzenli et al., 2020, Ford, J., 2013)
[0037] Within the scope of the method of the invention, the composition of erythrocytes, in particular the lipid composition, is now used as an independent compartment for the detection of various organic diseases, preferably liver diseases. The analysis of erythrocyte lipids is carried out using spectroscopic methods, particularly preferably mass spectrometry (MS).
[0038] Mass spectrometry (MS) is a widely used and well-established method for quantifying various molecular changes in different samples. The principle of mass spectrometry is to analyze the mass-charge quotient for a single molecule, which can be easily compared to a standard mixture or a global database technology. Molecules passing through the spectrometer must be desorbed in a first step, followed by an ionization step to introduce a specific molecular charge. Powerful turbopumps create a high vacuum in the MS, allowing the molecules to pass through without friction. The induced ions can then be accelerated and separated in electric fields to guide them to the surface of the detector. (Loos et al.)High-pressure liquid chromatography (HPLC) currently enables the effective separation of molecules in complex sample matrices through fractionation. Combining both techniques enables valuable measurements in various application areas (Wilson et al., 2005).
[0039] Lipidomics is a subfield of metaboimics that primarily focuses on various lipid subgroups involved in signaling, membrane, or metabolic pathways. (Luque et al., 2020, Züllig et al., 2020) Many recent studies demonstrate that lipids play a central role in the pathophysiology of sepsis and could become a central component of potential diagnostic and therapeutic targets. (Amungama et al., 2021)
[0040] Phospholipids, which are involved in various important molecular processes, can be divided into glycerophospholipids and sphingolipids. Both molecules are bound to phosphate but differ in the structure of the lipid backbone. While glycerophospholipids contain glycerol and two fatty acid chains, sphingolipids contain sphingosine and an additional fatty acid chain (see also Figure 2).
[0041] Preferred key metabolites for the method of the invention are therefore lipids from erythrocytes, preferably phospholipids and sphingolipids, particularly preferably lipids which are independently selected from the group comprising phosphatidylcholines (PC), ceramides (Cer), sphingomyelins (SM), lysophosphatidylethanolamines (LPE), and lysophosphatidylcholines (LPC).
[0042] Key metabolites particularly suitable for the process according to the invention are the lipids according to Table 1 (see Example 4).
[0043] Phosphatidylcholines suitable for the process according to the invention are selected, for example, from
[0044] PC (28:0), PC (30:0), PC (30: 1), PC (32:0), PC (32:1), PC (34: 1), PC (34:2), PC (36:0), PC (36: 1), PC (36:2), PC (36:3), PC (36:4), PC (38:0), PC (38: 1), PC (38:2), PC (38:3) and PC (38:4).
[0045] Sphingomyelins suitable for the method according to the invention are selected, for example, from
[0046] SM (14:0), SM (16:0), SM (18:0), SM (18: 1), SM (20:0), SM (22:0), SM (24:0), SM (24: 1), SM (26:0) and SM (26: 1).
[0047] Lysophosphatidylethanolamines suitable for the process according to the invention are selected, for example, from
[0048] LPE (16:0), LPE (16:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4).
[0049] Ceramides suitable for the process according to the invention are selected, for example, from
[0050] Cerium (14:0), Cerium (16:0), Cerium (18:0), Cerium (18:1), Cerium (20:0), Cerium (22:0), Cerium (24:0) and Cerium (24:1)
[0051] Lysophosphatidylcholines suitable for the process according to the invention are selected, for example, from
[0052] LPC (16:0), LPC (16:1), LPC (18:0), LPC (18:1), LPC (18:2), LPC (20:0), LPC (20:1), LPC (20:3) and LPC (20:4).
[0053] In the example compounds listed here, the numbers in parentheses symbolize the chain length of the lipid and the number of double bonds in the molecule (chain length of the lipid : number of double bonds in the molecule). A particular advantage of the invention is that the method provided can be used to analyze a combination of a large number of different lipids as key metabolites of erythrocytes. This allows changes in signatures of key metabolites in biological samples to be detected in a disease and used to diagnose a metabolic disease or disorder, particularly in the liver. This is not yet known in the prior art.
[0054] According to the invention, at least one mass spectrum of the biological sample is measured with at least one key metabolite.
[0055] In one embodiment of the invention, more than one mass spectrum is measured for more than one key metabolite of the biological sample. Preferably, between 2 and 100 mass spectra, more preferably between 2 and 75 or 2 and 50 mass spectra, and most preferably between two and ten or two and five mass spectra are measured for the corresponding number of key metabolites.
[0056] Particularly good results were achieved when the mass spectra of 57 key metabolites, in particular of the 57 lipids according to Table 1 (see Example 4), were measured.
[0057] Advantageously, mass spectrometry requires only small amounts of sample to be carried out. Accordingly, in one embodiment of the invention, between 1 μl and 150 μl, preferably between 30 μl and 100 μl, most preferably 50 μl of the biological sample is used for measurement by mass spectrometry. Since biological samples from the field of medical diagnostics have a limited volume, it is of great advantage that only a small portion of the biological sample is required to carry out the method according to the invention. This enables general, untargeted screening diagnostics for all samples before or in parallel with targeted conventional laboratory diagnostics. Nevertheless, a predominant portion of a biological sample is available for carrying out further diagnostics, such as routine medical diagnostics in the laboratory. Based on the determined differences in the type, quantity and / orThe pattern of key metabolites (collectively referred to herein as the "signature" of the key metabolites) present in a biological sample obtained from a subject compared to a normal control sample can be used to correlate the type or amount of one or more key metabolites with a likely diagnosis of a disease. A statistically significant increase in the amount of one or more key metabolites compared to the control samples is sufficiently predictive that the subject suffers from a disease typical for the key metabolite(s) or a specific pattern of key metabolites.A normal amount of one or more key metabolites or a normal pattern of key metabolites, as characteristic of a control sample isolated from a normal population, indicates that the patient does not have a disease typical of that key metabolite(s) or a particular pattern of key metabolites. Positive evidence of disease based on an increased or decreased amount of one or more key metabolites or an altered pattern of key metabolites compared to a normal control is generally considered, along with other factors, in the final determination of a specific disease.Therefore, the increased or decreased amounts of one or more key metabolites or an altered pattern of key metabolites in the tested subject are usually considered together with other accepted clinical features of the respective disease in making a definitive diagnosis of such a disease.
[0058] Key metabolites are disease-specific and may only be identified and detected using the method according to the invention.
[0059] In a particularly preferred embodiment, the method for analyzing a biological sample for changes in signatures of bowl metabolites in a liver disease comprises the steps
[0060] (a) Isolation of erythrocytes from the biological sample,
[0061] (b) identification and, where appropriate, quantification of erythrocyte lipids as key metabolites for liver and infectious disease, comprising the lipids PC (28:0), PC (30:0), PC (30:1), PC (32:0), PC (32:1), PC (34:0), PC (34:1), PC (34:2), PC (36:0), PC (36:1), PC (36:2), PC (36:3), PC (36:4), PC (38:0), PC (38:1), PC (38:2), PC (38:3) and PC (38:4);
[0062] SM (17:0), SM (12:0), SM (14:0), SM (16:0), SM (18:0), SM (18: 1), SM (20:0), SM (22:0), SM (24:0), SM (24: 1), SM (26:0) and SM (26: 1);
[0063] LPE (16:0), LPE (16:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4). Cerium (15:0), Cerium (12:0), Cerium (14:0), Cerium (16:0), Cerium (18:0), Cerium (18:1), Cerium (20:0), Cerium (22:0), Cerium (24:0) and Cerium (24:1);
[0064] LPC (16:0), LPC (16:1), LPC (17:0), LPC (18:0), LPC (18:1), LPC (18:2), LPC (20:0), LPC (20:1), LPC (20:3) and LPC (20:4), and
[0065] LPE (16:0), LPE (16:1), LPE (17:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4);
[0066] (c) principal component analysis of the samples and clustering by subgroups;
[0067] (d) identification of key metabolites using the XGBoost algorithm;
[0068] (e) detecting changes in the signatures of the key liver disease metabolites identified in step (d), and
[0069] (f) Diagnosis of liver disease.
[0070] The method according to the invention may further comprise conventional steps for sample preparation for mass spectrometry which are familiar to the person skilled in the art.
[0071] The invention further relates to the use of the method for analyzing a biological sample for changes in signatures of key metabolites in a disease for diagnosing a metabolic disease, metabolic disorder or a proliferative disease.
[0072] This concerns, for example, the isolation of erythrocytes from a biological sample. Whole blood from a test subject is preferably used for the isolation. During sampling, agents to prevent blood clotting, preferably ethylenediaminetetraacetic acid (EDTA), are added to the whole blood for the further processing steps. Further steps for isolating erythrocytes from a biological sample usually comprise sedimenting the erythrocytes by centrifugation (e.g. at 1,000 g for 10 min at 20 °C) to separate the cellular blood components, washing the sedimented erythrocytes with a buffer solution (e.g. PBS) and centrifuging them again (at 1,000 g for 10 minutes at 20 °C), and storing them at -80 °C. In one embodiment, the method according to the invention is an in vitro method and the step of taking the biological sample is not part of the method.
[0073] Further sample preparation steps are typically necessary for the analysis of key metabolites using mass spectrometry. It is particularly preferred that sample preparation be performed under standardized conditions to comply with Good Laboratory Practice (GLP) requirements and achieve reproducible results.
[0074] A stored sample of red blood cells is thawed, a predefined volume is taken and mixed with a predefined volume of a solvent and a predefined volume of an internal standard mixture (see Table 1).
[0075] In a particularly preferred embodiment of the method according to the invention, 80 pl of isolated erythrocytes are mixed with 790 pl of methanol and 10 pl of the internal standard mixture.
[0076] Sample preparation for mass spectrometry typically includes further steps known to those skilled in the art, such as: a step of rotating the mixture for a predefined period of time at room temperature; a step of precipitating overnight at a temperature of -80°C; a step of centrifuging; a step of evaporating the supernatant after centrifugation; a step of dissolving the evaporated sample in a solvent; and storing the sample at a temperature of -80°C.
[0077] The step of rotating the mixture for a predefined period of time at room temperature can be performed, for example, in a vortex mixer at high speed for 1 minute, followed by a rotary mixer for 20 minutes at 15 revolutions per minute. Centrifugation can be performed, for example, at 10,000 g for 10 minutes at 4°C.
[0078] The step of evaporating the supernatant can be carried out, for example, at 50°C for 90 min in a vacuum centrifuge.
[0079] The sample can then be resuspended in a solvent such as methanol, preferably 100 μl methanol.
[0080] The resuspended sample can then be directly analyzed by mass spectrometry or temporarily stored at a temperature of -80 °C.
[0081] In a particularly preferred embodiment, sample preparation for mass spectrometry comprises the following steps: a step of rotating a mixture of 80 μl of isolated erythrocytes with 790 μl of methanol and 10 μl of the internal standard mixture at room temperature for 1 min at high speed in a vortex mixer and then in a rotary mixer for 20 min at 15 revolutions per minute; a step of precipitation overnight at a temperature of -80°C; a step of centrifugation at 10,000 g for 10 min at 4°C; a step of evaporating the supernatant after centrifugation at 50°C for 90 min in a vacuum centrifuge; a step of dissolving the evaporated sample in 100 μl of methanol; and optionally storing the sample at a temperature of -80°C.
[0082] The invention further relates to the use of the method for analyzing a biological sample for changes in signatures of key metabolites in a disease for diagnosing a metabolic disease or metabolic disorder, in particular of the liver.
[0083] The “diagnosis of the disease” step of the method according to the invention then further comprises the steps of: • comparing the detected amount of one or more key metabolites in the biological sample with an amount of the key metabolites characteristic of a normal control sample (reference range); and / or
[0084] • Comparing the identity of one or more key metabolites in the biological sample with the key metabolites present in a normal control sample or reference range; wherein the presence of additional key metabolites or the absence of key metabolites, preferably the presence of additional key metabolites in the biological sample compared to the normal control sample, is a positive indicator of the disease; and / or
[0085] • Comparing the pattern of key metabolites in the biological sample with the pattern of key metabolites in a normal control sample or with a reference range; where the changes in the pattern of key metabolites in the biological sample compared to the normal control sample is a positive indicator of the disease.
[0086] In a particularly preferred variant, the method according to the invention is based on the examination of blood samples, whereby the analysis of the signatures of lipids or the lipid profile of erythrocytes is carried out.
[0087] This makes it possible to determine, based on the signature of the lipids, whether the tissue being examined, in particular the liver, and thus the test subject, is diseased or not.
[0088] The data evaluation of the method according to the invention usually comprises steps such as:
[0089] - Data validation;
[0090] - Interpretation;
[0091] Comparison with references; and
[0092] - Categorization.
[0093] In a particularly preferred embodiment of the method according to the invention, the diagnosis of metabolic changes in the liver according to step v. is carried out based on the changes in predefined lipid fractions, i.e. based on key metabolites / lipids that are typical of a specific liver disease. The key metabolites are preferably identified by correlation with a spectral reference library of pure substances. Such a procedure is known to the person skilled in the art. The step of analyzing and evaluating the metabolic signature is preferably carried out using statistical methods. The spectroscopy data can be analyzed by purely visual comparison in order to extract the disease-relevant information, or by applying suitable statistical methods that are known to the person skilled in the art and are selected. All unsupervised and supervised statistical methods are suitable for this purpose.Supervised statistical methods include, for example, principal component analysis. Supervised statistical models include, for example, linear discriminant analysis, support vector machines, partial least squares regression, random forests, neural network analysis, and machine learning. Furthermore, methods for sample classification and visualization of component distributions (alone or in combination with supervised and unsupervised statistical methods) can be used. Suitable methods for sample classification and visualization include, for example, abundance analyses and cluster analysis methods. These include, for example, vector component analysis, NFINDR, and k-means clustering.
[0094] Principal component analysis is particularly preferably used in the method according to the invention to analyze and evaluate the metabolic signature. The evaluation of data using principal component analysis is known to those skilled in the art; see, for example, https: / / de.wikipedia.org / wiki / Hauptkomponentenanalyse.
[0095] It is further preferred if the principal component analysis of the measured erythrocyte lipid profiles is subsequently used in a cluster analysis. The implementation of cluster analyses is also known to those skilled in the art; see, for example, https: / / de.wikipedia.org / wiki / Clusteranalyse.
[0096] A liver disease that can be diagnosed using the method according to the invention is, for example, a disease selected from
[0097] - toxic, acute liver damage; obstructive, cholestatic liver damage;
[0098] - ischemic liver damage; - fibrotic, chronic liver damage; and septic liver damage.
[0099] Typical key metabolites / lipids for toxic, acute liver damage are gamma-glutaryltransferase, alanine aminotransferase and aspartate aminotransferase, glutamate dehydrogenase.
[0100] Typical key metabolites / lipids for obstructive, cholestatic liver damage are gamma-glutaryltransferase, alkaline phosphatase, direct and indirect bilirubin, taurine- or glycine-conjugated and unconjugated bile acids (such as litocholic acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, obeticholic acid,
[0101] Dehy drocholic acid) .
[0102] Typical key metabolites / lipids for ischemic liver damage: alanine aminotransferase and aspartate aminotransferase.
[0103] Typical key metabolites / lipids for fibrotic, chronic liver damage are synthesis parameters (such as albumin, coagulation factors, haptoglobin), alanine aminotransferase, aspartate aminotransferase and cholinesterase.
[0104] Typical key metabolites / lipids for septic liver damage are alanine aminotransferase, aspartate aminotransferase and glutamate dehydrogenase.
[0105] The invention offers numerous advantages. It enables the identification of metabolites in erythrocytes using statistical methods, such as abundance mapping and cluster analyses. The erythrocytes can thus be used as "metabolic marker cells" for the identification of pathophysiological metabolic changes. The method according to the invention now allows targeted metabolomics in erythrocytes based on mass spectrometry. Furthermore, the method allows the analysis of the lipid profile in erythrocytes, which can provide information about the severity of a disease progression or the stage of a disease, particularly of the liver.
[0106] As part of functional diagnostics in everyday clinical practice, so-called metabolic fingerprints can be created and used to identify liver diseases. Due to the much larger amount of information processed in the method according to the invention, more precise diagnostics are possible than with laboratory diagnostics.
[0107] The results of the method according to the invention can be advantageously used in personalized medicine. Early diagnosis is enabled. Furthermore, the results of the method according to the invention can be used to prevent progressive organ failure. Therapies can be individually adapted and implemented more specifically.
[0108] As described in the exemplary embodiments, the method according to the invention was able to distinguish between septic and non-septic conditions in mammals. The lipid composition of the erythrocytes was analyzed using high-performance liquid chromatography and triple quadrupole mass spectrometry. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) were performed to cluster the animal cohorts. XGboost, implemented in R, was used to identify critical metabolites.
[0109] Lipidomic analysis revealed that it is possible to distinguish septic from healthy individuals based on the lipid composition of the red blood cells. Furthermore, PCA reveals two clusters of animals that correspond to the underlying groups. Thus, healthy and septic animals can be separated through dimensionality reduction. XGBoost was successfully trained for the classification of PCI animals and tested using cross-validation. In addition to successfully identifying animal groups, XGBoost was also used to determine which features are informative for distinguishing between PCI animals and other groups. The results showed that the lipid composition of the red blood cells was at least as informative as the conventional use of cytokines.
[0110] Lipid composition was analyzed using HPLC / MS to quantify the chemical composition of the red blood cell membrane. The panel of 57 lipids (see Table 1 above) was analyzed to create a clustering model attempting to distinguish between septic and non-septic conditions. To meet these expectations, statistical methods were used to visualize the dataset to enable clear clustering.
[0111] The first step was to conduct a principal component analysis (PCA), shown in Figures 3 and 4, to reduce the number of variables. PCA seeks orthogonal vectors that capture the variance of the data set in as few dimensions as possible. PCA dimensions are linear combinations of the original dimensions. This makes it possible to infer the variable of the original data set from the vector. This makes it possible to determine which metabolites contribute most to clustering. By combining different dimensions, it is possible to determine which parameters allow a clear distinction between the groups.
[0112] To further illustrate the hierarchical relationships between the two experimental cohorts and the measured metabolites, a heatmap was created. This two-dimensional visualization further helps identify separations and connections within the dataset. The hierarchical cluster, represented as a dendrogram at the edge of the heatmap, provides additional insight into the combinations and effects of the data points.
[0113] In summary, the differences between PCI- and sham-treated animals are concentrated in PC(28:0), PC(36:4), LPC(18:2), and LPC(16:0), highlighting the specificity of these lipid metabolites in the erythrocyte membrane. Thus, septic and healthy animals can be divided into groups based on the lipid composition of the erythrocytes.
[0114] In a preferred embodiment of the method according to the invention, the lipids PC(28:0), PC(36:4), LPC(18:2) and LPC(16:0) are therefore identified as key metabolites.
[0115] In a particularly preferred embodiment of the method according to the invention, the lipids PC(28:0), PC(36:4), LPC(18:2), and LPC(16:0) are identified as key metabolites for the diagnosis of peritoneal contamination and infection (PCI) and sepsis, respectively. Sepsis is a life-threatening condition resulting from an overwhelming immune response of the body to an infection. It is a medical emergency that requires immediate diagnosis and treatment. Early diagnosis of sepsis is crucial to prevent serious complications and improve patient outcomes. Current diagnostics rely primarily on rather nonspecific laboratory values. Blood culture is the gold standard for the detection of bloodstream infections. In addition, imaging techniques and scoring systems enable further quantification of the extent of sepsis-related organ damage.The need for new diagnostic procedures, especially those that detect sepsis, remains unbroken in order to reduce the number of sepsis-related deaths.
[0116] The invention deals with the explicit evaluation of the use of lipidomic profiles of erythrocytes in a human patient collective.
[0117] Lipid analysis was performed using PCA to reduce data complexity and improve interpretability. As shown in Figure 3A, the first three dimensions are crucial for clustering the dataset by lipid profile, comprising 74.2% of the variables. According to the correlation plot (Figure 3B), different lipid subtypes are essential for each dimension. Interestingly, sphingomyelin, phosphatidylcholine, and lysophosphatidylcholines are represented in the first dimension. The most prominent unit is PC(36:3), which is not known to play a crucial role in sepsis pathology. Short-chain and long-chain sphingomyelins are also crucial for the classification process of erythrocytes. These findings are consistent with the notion of SM expression in inflammation and sepsis. SM is a major component of lipid structures required for various cellular processes, e.g.,for cellular signaling, membrane trafficking, and interaction with pathogens. In comparison, the second dimension consists of a few variables, including C14-SM and several PCs (PC(28:0) and PC(30:1) > 0.5). The third dimension, which accounts for 13.8% of the explained variables, consists of individual lipid metabolites. SolP, C26-SM, and PC(32:1) are the most important. SolP is a quite relevant and bioactive lipid in inflammation and vascular development.
[0118] In summary, the lipidomic analysis performed according to the present invention is a phenomenological approach to interpreting erythrocyte mass spectrometry results. It was able to significantly distinguish septic mice from control mice.
[0119] The invention has demonstrated that erythrocytes are an interesting cell type for clinical sepsis diagnosis. Erythrocytes are easily obtained from blood samples and are simple to process. Obtaining molecular information about their metabolic composition allows us to better understand the pathophysiology of sepsis and related diseases, enabling better diagnosis and tailored treatment strategies.
[0120] The invention is explained in more detail below with reference to 9 drawings and 5 embodiments.
[0121] They show:
[0122] Figure 1 shows the isolation of erythrocytes and membrane composition (created with BioRender.com);
[0123] Figure 2 shows the basic structure of various phospho- and sphingolipids, where R, Ri and R2 can be fatty acid residues of different chain lengths (modified from Cui, L., & Decker, EA (2016). Phospholipids in foods: prooxidants or antioxidants? Journal of the science of food and agriculture, 96(1), 18-31;
[0124] Figure 3 shows a scree plot (Fig. 3A) and corrplot analysis (Fig. 3B) for the principal component analysis. The scree plot (Fig. 3A) shows the eigenvalues for each dimension in descending order. The first three dimensions account for approximately 74.2% of the explained variance in the PCA. Fig. 3B: The corrplot shown here illustrates the significance of the individual measured metabolites for the dimensions.
[0125] Figure 4: The principal component analysis based on all lipids. In this
[0126] Three different PCAs are shown in the figure. All are based on the complete dataset and use all lipids that were measured. Dimensions: Fig. 4A 1:2; Fig. 4B 1:3; Fig. 4C 2:3. Figure 5: a scree plot (Fig. 5A) and PCAs (Figs. 5B, 5C, and 5D) after
[0127] Factor reduction. The lipid metabolites were reduced according to the corrplot in Fig. 4B, using a cutoff of 0.7. A scree plot with a reduced number of factors. The first three dimensions account for 86.8% of the explained variance. Fig. 5B PCA with dimensions 1:2; Fig. 5C 1:3; and Fig. 5D 2:3.
[0128] Figure 6: The contribution of lipid components to the cluster analysis. Shown are six
[0129] Lipids, PC(28:0), PC(36:4), LPC(18:2), LPC(16:0), C18:9 SM and C24:1 SM, and their contributions to the cluster analysis with a test error = 0.
[0130] Figure 7: the principal component analysis based on the 3 key metabolites
[0131] PC(28:0), PC(36:4), LPC(18:2).
[0132] Figure 8 shows the principal component analysis for evaluating the lipid data obtained by mass spectrometry from a previous animal experiment. The following are:
[0133] CONTROL Healthy control group
[0134] AP AP Model of toxic, acute liver injury BDL Model of obstructive, cholestatic liver injury
[0135] IR model of ischemic liver damage
[0136] TAA model for fibrotic, chronic liver damage
[0137] PCI model for septic liver damage
[0138] Figure 9: the schematic sequence of the method according to the invention
[0139] 110 Sample collection, including provision of EDTA whole blood, isolation of erythrocytes, purification of erythrocytes;
[0140] 120 mass spectrometry measurements, including sample purification according to standardized SOP, carrying out the measurements with defined standards;
[0141] 130 Data analysis, including data validation,
[0142] Interpretation,
[0143] Comparison with references, categorization; and
[0144] 140 Individualized Medicine, Comprehensive
[0145] Early diagnosis
[0146] Prevention of progressive organ failure Adapted and targeted therapy
[0147] Example 1: Isolation of erythrocytes from a whole blood sample
[0148] 1.1 Animal model for peritoneal contamination and infection (PCI)
[0149] Mice (n=18) with peritoneal contamination were infected using a human fecal suspension. All animals were weighed and scored, e.g., using the Clinical Severity Score (CSS). Classification was performed according to Table 2. Approximately 60–70 μl of human fecal suspension diluted in Ringer's acetate solution (Berlin Chemie RG, Germany) was injected intraperitoneally. Sham-treated animals (n=14) received an injection of adequate Ringer's acetate solution intraperitoneally. The animals were assessed after 6 hours, 12 hours, 15 hours, 18 hours, and 21 hours and weighed at least twice daily. Novaminsulfone (2.5 mg, Ratiopharm, Germany) was administered orally (po) every 6 hours after infection. In addition, meropenem (25 mg kg-1 ; Inresa Arzneimittel GmbH, Germany) diluted in Ringer's acetate (2.5 mg mL-1 ) was injected subcutaneously twice, six hours and 18 hours after infection.
[0150] Table 2: CSS grading system used for organ procurement.
[0151] 1.2 Blood sampling and erythrocyte isolation
[0152] Whole blood samples were transferred into 1.2 ml ethylenediaminetetraacetic acid (EDTA) Monovettes (Sarstedt, Germany) to prevent blood clotting for further processing steps. After initial centrifugation of the Monovettes at 1,000 g for 10 minutes at 20°C, the plasma fraction was transferred to fresh polypropylene tubes and stored at -80°C for further analysis. The remaining red blood cells were washed twice by replacing the amount of extracted plasma with PBS (Biozym, Germany). After adding approximately 200 μl of PBS, the samples were centrifuged again at 1,000 g for 10 minutes at 20°C. This supernatant exchange step was performed twice. After final removal of the supernatant, the red blood cells were aliquoted into polypropylene tubes (Eppendorf, Germany), weighed, and stored at -80°C.
[0153] Example 2: Sample preparation for mass spectrometry
[0154] Sample preparation for mass spectrometry was performed according to a standard operating procedure.
[0155] Materials used
[0156] Methanol (>99.9%)
[0157] Internal standard mixture (see Table 1) glass vials
[0158] Devices used
[0159] - Rotator IKA-Loopster Tissuelyzer Qiagen
[0160] - Centrifuge Speed Vac
[0161] - Horizontal shaker
[0162] Vortex mixer
[0163] Test procedure
[0164] A weighted aliquot of approximately 80 μl of erythrocytes was used for the measurements. Preparation for lipid quantification began with the addition of 790 μl of methanol (>99.9%, Honeywell Riedel-de-Haen, Germany) and 10 μl of internal standard mixture (each 30 pmol per injection volume; for details, see Table 1 in Example 4) as a reference for normalization and quantification to the tubes containing the erythrocyte samples. After precipitation overnight at -80°C, the samples were centrifuged at 10,000 g for 1 hour.
[0165] The supernatant was transferred to a new polypropylene tube and evaporated in an SPD SpeedVac (Thermo Scientific Savant) at 50°C for 90 min. Subsequently, reconstitution was performed in 100 pL methanol (> 99.9%) on a plate shaker at maximum speed for 1 min. Finally, the samples were transferred to glass vials (Dr. R. Forche Chromatographie, Germany) and sealed.
[0166] Depending on the availability of the mass spectrometer, the sample was then measured immediately or temporarily stored at -80°C.
[0167] Example 3: Metabolic analysis
[0168] Metabolomic analyses were performed using mass spectrometry to measure the lipid composition of erythrocytes and to demonstrate the changes in the different lipid fractions to further characterize disease at a molecular level.
[0169] The assessment of liver excretory function was mainly performed by bile acid measurements in spleen and liver samples.
[0170] Therefore, mass spectrometry was the most efficient method to identify the overall changes in different organic compartments.
[0171] Example 4: Lipid Identification in Erythrocytes The lipid profile was measured in erythrocytes. Sample preparation for lipid quantification was performed as described in Example 2. For the internal standard mixture (30 pmol per injection volume) as a reference for normalization and quantification, see Table 1 below.
[0172] For the measurements, an L-7250 autosampler, a Merck Peltier sample cooler for the L-7250, an L-2130 liquid chromatograph with an L-2300 column oven, a 60x2 mm MultoHigh 100 RP 18 column with a 3 pm particle size (CS Chromatography Service, Germany), and an Elite LaChrom LC system (Hitachi Europe GmbH, Germany) were combined and maintained at 50 °C. Mobile phase A consisted of 1.0% (v / v) formic acid in ddH2O, and mobile phase B consisted of 100% methanol (Chromasolv, Honeywell Riedel-de Haen, Seelze, Germany). The flow rate was 0.5 ml min. 1The column was immersed in 10% B. The mobile phase changed to 100% B after sample injection. The flow rate increased from 0.5 ml min' 1 at 5 min linear to 1.0 ml min' 1 at 7 min and remained constant until 10 min. After that, the mobile phase was switched to 10% B, and the flow rate decreased linearly from 1.0 ml min' 1 at 10 min to 0.5 ml min' 1 at 10.5 min and remained constant at 11.3 min until the end of the program. Detection was performed between 2 and 10 min.
[0173] All samples were cooled to 4 °C, and 10.0 μl per sample was injected. The high-pressure liquid chromatography system equipped with an ESI or APCI source was combined with the API 2000 triple quadrupole mass spectrometer (Sciex, Foster City, CA, USA), both operating in positive mode under specific source parameters: source temperature 450 °C, curtain gas 40, collision gas "low", ion spray voltage 5500, ion source gas 1 60, ion source gas 2 30. The target ions Q1 > Q3, the assignment to the internal standard, and the type of analysis are given in Table 1 below. All results were quantified using Analyst 1.6.2 (AB Sciex, Foster City, CA, USA) based on internal standard samples and the external standard curve.
[0174] Table 1: List of target ions / MRM transitions, assignment to internal standard and analysis mode
[0175] Example 5: Lipid quantification in erythrocytes
[0176] 5.1 Principal component analysis
[0177] The scree plot shown in Figure 3A was created before the dimensions were selected for creating the PCA plots. It visualizes the eigenvalues of the principal components in the data analysis to assume the weight of each calculated dimension for the cluster analysis. The first dimension represents 44.8% of the explained variance and symbolizes almost half of the explained variables. This is the central part compared to the following data. Dimensions two and three account for 15.6% and 13.8% within the dataset, respectively.
[0178] Each subsequent dimension accounts for less than 10% and is therefore not used in the generated PCAs. The cutoff was determined individually based on the generated percentages. The inclusion of PCA dimensions was unnecessary and inappropriate, as it only affected a tiny fraction of the variables used to categorize the animals. Had further analysis revealed the need, our algorithm would have been modified to improve the clustering of the dataset.
[0179] Figure 3B, on the other hand, shows the effects of the measured lipids for each dimension. The first five dimensions are shown to illustrate the differences between them. Analysis of the first dimension reveals a clear structure. Sphingomyelins (SM), phosphatidylcholines (PC), lysophosphatidylethanolamines (LPE), and lysophosphatidylcholines (LPC) are predominantly present. Based on correlation, PC(36:3), PC(36:4), and (PC34:1) are the most important factors in distinguishing the dimensions in the cluster analysis, with p-values of p > 0.85.
[0180] Furthermore, some lipid metabolites do not appear to influence the first dimension. Short-chain sphingomyelins (CI 2 SM and C14 SM) are mainly weighted in dimension 2. PC(28:0) and PC(30:1) also show a higher correlation magnitude for the second dimension. Together with a small proportion of LPCs (mainly LPC 20:1 and 20:3) and LPE(18:0), the second dimension is structured differently by different lipid classes. It is therefore comparable to the first dimension, although fewer metabolites are present in the second dimension. The third dimension differs significantly from the first two, consisting of only a few metabolites with high correlation strength. D18:1SolP, C26 SM, PC(32:1), and PC(38:2) are characteristic of this dimension. Furthermore, the LPEs appear to influence this dimension. With the exception of LPE(18:0), which is also found in the second dimension, LPEs are mainly found in the third dimension.
[0181] These results indicate high variability and heterogeneity within and between dimensions. Principal component analysis was calculated using R based on the results presented in Figure 3. Figure 4 shows the PCA plots for the first three dimensions to illustrate the distribution of animals within the dimensional system.
[0182] As shown in Figure 4A, it is possible to distinguish two independent populations based on dimensions one and two, while in the opposite group only a few animals are seen.
[0183] While the representation of dimensions one and two results in good clustering, dimensions two and three do not reveal any significant clusters (Fig. 4B).
[0184] Both cohorts are homogeneously mixed, which makes it difficult to separate septic and healthy animals based on these metabolites.
[0185] Similar to the first representation, dimensions two and three show a visible clustering of sepsis and non-sepsis. Each entity can be divided into two vertically distinct subunits (in dimension 2) that show no obvious correlation with the dataset itself (Fig. 4C).
[0186] 5.2 Factor reduction and PC A
[0187] With regard to clinical applicability, further investigations were conducted into whether it is possible to reduce the amount of lipid metabolites to facilitate cluster analysis. Use as a biomarker should focus on a small panel to meet the requirements for good clustering, rather than measuring more than 50 parameters, which is time-consuming and impossible in daily practice.
[0188] Figure 5A shows the scree plot for the reduced number of variables. 53.2% of the explained variance is attributable to the first dimension, which is higher than in Figure 3A and symbolizes more than half of the variables. Dimensions two and three account for a further 20.6% and 13% of the explained variance, respectively.
[0189] The PCAs for the first three dimensions are shown in Figures 5B, 5C, and 5D. Based on the above findings, clustering the data with fewer variables was also possible.
[0190] In Fig. 5B, a clear separation is not possible, but there is a clear tendency to group septic and healthy animals together. For other dimensional combinations, such as 3:1 (Fig. 5C) and 3:2 (Fig. 5D), this separation is less practical than for the first two.
[0191] 5.3 Identification of key lipids
[0192] The PCA analysis demonstrated that it is possible to visually distinguish between septic and healthy animals based on the lipid profile of the red blood cells. The following steps involved further analysis of the lipid dataset. The aim was to investigate which lipid metabolites contribute to clustering the dataset based on pathophysiology.
[0193] Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) is therefore an excellent approach to dimensionality reduction for determining the similarity of data points. The data points are related to their k-nearest neighbors using a nonlinear kernel and then further projected down to the number of dimensions. This makes it possible to extract the essential variables of the dataset.
[0194] The UMAP and the previously performed PCA suggest that it should be possible to distinguish between PCI- and sham-treated animals based on lipidomics analysis of erythrocytes. XGBoost, implemented in R (Chen & Guestrin), was used for this purpose. Ten cross-validation steps with a 90 / 10 split were performed, resulting in an average classification error of 18%. Considering the factors that contribute most to classification, the lipids PC(28:0), PC(36:4), LPC(18:2), and LPC(16:0) are the main factors (Fig. 6), with the lipids PC(28:0), PC(36:4), and LPC(18:2) being dominant. Figure 7 shows the principal component analysis based on the three main metabolites PC(28:0), PC(36:4), and LPC(18:2). The PCA shows the clustering of septic and healthy individuals between dimension one and dimension two.
[0195] 5.4 Summary
[0196] The lipid composition of the erythrocytes was analyzed using high-performance liquid chromatography and triple quadrupole mass spectrometry. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) were performed to cluster the animal cohorts. XGboost, implemented in R, was used to identify critical metabolites.
[0197] Lipidomic analysis revealed that it is possible to distinguish septic from healthy individuals based on the lipid composition of the red blood cells. Furthermore, PCA reveals two clusters of animals that correspond to the underlying groups. Thus, healthy and septic animals can be separated through dimensionality reduction. XGBoost was successfully trained for the classification of PCI animals and tested using cross-validation. In addition to successfully identifying animal groups, XGBoost was also used to determine which features are informative for distinguishing between PCI animals and other groups. The results showed that the lipid composition of the red blood cells was at least as informative as the conventional use of cytokines.
[0198] 5.5 Determination of other liver damage using principal component analysis
[0199] The principal component analysis in Figure 8 is also based on the experimentally collected data on the lipid composition of erythrocytes. The principal component analysis was performed using R. Dimensions 1 and 2 are represented, with each point shown corresponding to an individual. The symbols indicate the assignment to the corresponding animal model.
[0200] The result of the principal component analysis for the evaluation of lipid data obtained by mass spectrometry from a previous animal experiment is shown in Figure 4. In Figure 4, the following mean:
[0201] CONTROL Healthy control group
[0202] AP AP Model of toxic acute liver injury
[0203] BDL model for obstructive cholestatic liver damage
[0204] IR model for ischemic liver damage
[0205] TAA model for fibrotic, chronic liver damage
[0206] PCI model for septic liver damage
[0207] This analysis already reveals the first clear cluster structures and groupings. It therefore seems plausible to detect various entities of liver dysfunction using obtained red blood cells and subsequent mass spectrometric measurements. The animal experiments conducted in this way are based on various animal models of different forms of liver dysfunction. The corresponding entities can be found in the list above.
[0208] References
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[0211] Vanwijngaerden YM, Langouche L, Brunner R, Debayeve Y, Gielen M, Casaer M, et al. Withholding parenteral nutrition during critical illness increases plasma bilirubin but lowers the incidence of biliary sludge. Hepatology 2014; 60: 202- 210.
[0212] Kruger PS, Freir NM, Venkatesh B, Robertson TA, Roberts MS, Jones M. A preliminary study of atorvastatin plasma concentrations in critically ill patients with sepsis. Intensive Care Med 2009- 35: 717- 721.
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[0215] Kuhn, V., Diederich, L., Keller, T., 4th, Kramer, C. M., Lückstädt, W., Panknin, C., Suvorava, T., Isakson, B. E., Keim, M., & Cortese-Krott, M. M. (2017). Red Blood Cell Function and Dysfunction: Redox Regulation, Nitric Oxide Metabolism, Anemia. Antioxidants & redox signaling, 26(13), 718-742. https: / / doi.org / 10.1089 / ars.2016.6954 Sun, P., Jia, J., Fan, F., Zhao, J., Huo, Y., Ganesh, S. K., & Zhang, Y. (2021). Hemoglobin and erythrocyte count are independently and positively associated with arterial stiffness in a community -based study. Journal of human hypertension, 35(3), 265-273. https: / / doi.org / 10.1038 / s41371-020-0332-6
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[0217] Düzenli Kar, Y., Özdemir, Z. C., Emir, B., & Bör, Ö. (2020). Erythrocyte Indices as Differential Diagnostic Biomarkers of Iron Deficiency Anemia and Thalassemia. Journal of pediatric hematology / oncology, 42(3), 208-213. https: / / doi.org / 10.1097 / MPH.0000000000001597
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Claims
Patent claims 1. A method for analyzing a biological sample for changes in signatures of Schüssel metabolites in a liver disease, comprising (a) the isolation of erythrocytes from the biological sample, (b) the identification and, where appropriate, quantification of erythrocyte lipids as key metabolites for liver and infectious disease, comprising the lipids PC (28:0), PC (30:0), PC (30:1), PC (32:0), PC (32:1), PC (34:0), PC (34:1), PC (34:2), PC (36:0), PC (36:1), PC (36:2), PC (36:3), PC (36:4), PC (38:0), PC (38:1), PC (38:2), PC (38:3) and PC (38:4); SM (17:0), SM (12:0), SM (14:0), SM (16:0), SM (18:0), SM (18: 1), SM (20:0), SM (22:0), SM (24:0), SM (24: 1), SM (26:0) and SM (26: 1); LPE (16:0), LPE (16:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4). Cerium (15:0), Cerium (12:0), Cerium (14:0), Cerium (16:0), Cerium (18:0), Cerium (18:1), Cerium (20:0), Cerium (22:0), Cerium (24:0) and Cerium (24:1); LPC (16:0), LPC (16:1), LPC (17:0), LPC (18:0), LPC (18:1), LPC (18:2), LPC (20:0), LPC (20:1), LPC (20:3) and LPC (20:4), and LPE (16:0), LPE (16:1), LPE (17:1), LPE (18:0), LPE (18:1), LPE (18:2) and LPE (20:4); (c) principal component analysis of the samples and clustering by subgroups; (d) identification of key metabolites using the XGBoost algorithm; (e) detecting changes in the signatures of the key liver disease metabolites identified in step (d), and (f) the diagnosis of liver disease.
2. Method according to claim 1, characterized in that the blood sample is EDTA whole blood.
3. The method according to claim 1 or 2, further comprising after step (d) the step (dl) Principal component analysis of the samples and clustering based on the key metabolites identified in step (d). Method according to one of the preceding claims, characterized in that the key metabolites are PC(28:0), PC(36:4), LPC(18:2). Method according to one of the preceding claims, characterized in that the isolation of the erythrocytes from the blood sample according to step (a) of claim 1 comprises centrifuging the blood sample to separate the cellular blood components and washing the isolated erythrocytes with a buffer solution. Method according to one of the preceding claims, characterized in that the sample processing according to step (a) of claim 1 comprises mixing a predefined volume of the isolated erythrocytes with a predefined volume of a solvent and a predefined volume of an internal standard mixture. Method according to claim 6, characterized in that 80 μl of isolated erythrocytes are mixed with 790 μl of methanol and 10 μl of the internal standard mixture.Method according to one of claims 5, 6 or 7, characterized in that the sample processing according to step (a) of claim 1 further comprises: a step of rotating the mixture for a predefined period of time at room temperature; a step of precipitating overnight at a temperature of -80°C; a step of centrifuging; a step of evaporating the supernatant after centrifugation; a step of dissolving the evaporated sample in a solvent; and storing the sample at a temperature of -80°C. Method according to one of the preceding claims, characterized in that the identification and optionally quantification of the lipids of the erythrocytes according to step (b) of claim 1 is carried out by means of mass spectrometry. Method according to one of the preceding claims, characterized in that the data evaluation comprises the parts of the method according to steps (b), (c), (d) and (d1) of. Claim 1 comprises the steps: - Data analysis - Data validation; - Modeling - Interpretation; Comparison with references; and - Categorization. Method according to one of the preceding claims, characterized in that by means of the principal component analysis of the lipid data obtained by mass spectrometry, liver damage, selected from - toxic, acute liver damage; obstructive, cholestatic liver damage; - ischemic liver damage; - fibrotic, chronic liver damage; and septic liver damage. Method according to one of the preceding claims, characterized in that septic liver damage is diagnosed using principal component analysis of the lipid data obtained by mass spectrometry.