CN115128175A - Biomarkers for detecting non-alcoholic fatty liver disease status - Google Patents
Biomarkers for detecting non-alcoholic fatty liver disease status Download PDFInfo
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- CN115128175A CN115128175A CN202110323111.6A CN202110323111A CN115128175A CN 115128175 A CN115128175 A CN 115128175A CN 202110323111 A CN202110323111 A CN 202110323111A CN 115128175 A CN115128175 A CN 115128175A
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Abstract
The invention provides a biomarker combination for detecting the non-alcoholic fatty liver disease state of a subject, which comprises hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate which are derived from a biological sample of the subject; the non-alcoholic fatty liver disease comprises simple non-alcoholic fatty liver and different stages thereof, non-alcoholic steatohepatitis and non-alcoholic steatohepatitis with different stages of fibrosis. The diagnosis device can quickly, accurately and comprehensively evaluate and diagnose the non-alcoholic fatty liver disease state of a tested body.
Description
Technical Field
The invention belongs to the technical field of biological detection, and particularly relates to a biomarker for detecting a non-alcoholic fatty liver disease state and application thereof. The invention also relates to a method for monitoring the treatment effect of the non-alcoholic fatty liver disease and evaluating the treatment medicine.
Background
Fatty liver (fatty liver) refers to a pathological condition of fat accumulation in liver cells caused by various reasons, and is a common pathological change of liver rather than an independent disease. Fatty liver disease seriously threatens the health of people in China, is the second most serious liver disease of viral hepatitis, has continuously increased incidence rate and is younger in attack age. Normal human liver tissue contains a small amount of fat, such as triglycerides, phospholipids, glycolipids, and cholesterol, and its weight is about 3% to 5% of the weight of the liver, and if too much fat accumulates in the liver, it exceeds 5% of the weight of the liver or when there is steatosis in more than 50% of the liver cells histologically, it is called fatty liver. The mild case has no symptoms, and the severe case has fierce illness. Generally, the fatty liver belongs to reversible diseases, and the early diagnosis and timely treatment can always restore the normal state.
Non-alcoholic fatty liver disease (NAFLD) refers to a clinical pathological syndrome characterized mainly by excessive deposition of fat in liver cells due to alcohol and other definite liver damage factors, and acquired metabolic stress liver injury closely related to insulin resistance and genetic susceptibility. Including simple fatty liver (also known as nonalcoholic steatohepatitis), nonalcoholic steatohepatitis (NASH), and its associated liver fibrosis and cirrhosis. With the prevalence trend of obesity and related metabolic syndrome globalization, the nonalcoholic fatty liver disease becomes an important cause of chronic liver disease in developed countries such as Europe and America and affluent areas in China, the prevalence rate of NAFLD of common adults is 10% -30%, wherein 10% -20% of NAFLD is developed into NASH, and the incidence rate of cirrhosis in 10 years in NASH reaches 25%.
Non-alcoholic fatty liver disease can directly cause decompensated liver cirrhosis, hepatocellular carcinoma and relapse of transplanted liver, can affect the progress of other chronic liver diseases, and is involved in the onset of type 2 diabetes and atherosclerosis. Malignant tumors related to metabolic syndrome, arteriosclerotic cardiovascular and cerebrovascular diseases and liver cirrhosis are important factors influencing the quality of life and the life expectancy of non-alcoholic fatty liver patients. For this reason, non-alcoholic fatty liver disease is a new challenge in the contemporary medical field, and the harm of non-alcoholic fatty liver disease to human health will continue to increase in the near future.
The prognosis of most non-alcoholic fatty liver diseases is good, the liver histology is slow in progress and even in a static state, and the prognosis is relatively good. Even if some patients have complicated steatohepatitis and hepatic fibrosis, if the patients can be diagnosed and treated in time, the change of the liver histology can still be reversed, and the fattiness of the patients is rarely caused by rupture of the fat cyst and fat embolism. A small number of steatohepatitis patients progress to cirrhosis, and the prognosis is poor once cirrhosis has occurred. For most fatty liver patients, the purposes of controlling body weight and blood sugar, reducing blood fat and promoting reversal of liver histology can be achieved by non-drug treatment measures such as diet control and moderate aerobic exercise adherence.
Since these diseases may be reversed or at least of limited consequence if they can be diagnosed as early as possible, it is of great interest to be able to provide new tools in the medical field that allow such an early, fast and accurate diagnosis. Although scientists currently propose non-invasive methods for diagnosing NASH and determining the activity, stage or severity of NASH, liver biopsy remains today the best method for distinguishing NASH from early stage steatosis. Steatosis, lobular and portal inflammation, hepatocyte damage as a form of swelling and apoptosis, and fibrosis are features of NASH assessed from biopsies. However, liver tissue biopsy has a number of significant drawbacks. First, the material collected in liver biopsy represents only a very small portion of the subject's liver, and therefore raises the question of whether the collected sample represents the overall status of the subject's organ. In addition, liver biopsy is a very invasive procedure that can be cumbersome, worrisome and painful for the patient, and carries concerns about morbidity and mortality. Finally, in view of the above, liver biopsy cannot reasonably be proposed as a routine procedure for determining whether individuals in the general population, or even patients at risk for NASH, have NASH, and/or for determining the activity, stage or severity of NASH in said individuals.
Ultrasonography is also used to diagnose hepatic steatosis. However, this method is subjective because it is based on the strength of the echo (echo strength) and the special pattern of the echo (texture). As a result, it is not sensitive enough and often inaccurate, especially in patients with advanced fibrosis.
Furthermore, several experiments have been performed in recent years to develop the use of non-invasive biomarkers of importance in the field of liver diagnosis. For example, EP1846862B describes a non-invasive in vitro method for diagnosing alcoholic or non-alcoholic steatohepatitis from a serum or plasma sample of a patient, said method comprising the steps of measuring the concentrations of 7 biochemical markers and then combining them by means of a logistic function in order to obtain a final value. In WO 2014/049131 a blood test for non-invasive diagnosis of non-alcoholic steatohepatitis is disclosed, which is based on the measurement of at least one biomarker reflecting apoptosis, at least one biomarker reflecting anthropometry, at least one biomarker reflecting metabolic activity and optionally at least one biomarker reflecting liver status, and a combination between said biomarkers in a mathematical function.
No such precise diagnostic method is currently available for each stage of NASH. It is therefore of great interest to provide an accurate, non-invasive tool for diagnosing NASH and determining the activity, stage and severity of NASH in a diagnostic subject.
In view of the above disadvantages, the present invention aims to provide a diagnostic device and biomarker which can be used for non-invasive detection and accurate classification of non-alcoholic fatty liver disease, and applications thereof.
Disclosure of Invention
The invention aims to provide a group of biomarker combinations, which can be used for diagnosing the non-alcoholic fatty liver disease of a subject, comprehensively, quickly and accurately grading the state and the process of the non-alcoholic fatty liver disease by detecting the biomarkers, can be used for assisting in monitoring the treatment effect in the diagnosis, auxiliary diagnosis and treatment process of the non-alcoholic fatty liver disease, and can be used for evaluating the treatment effect of a treatment medicament for treating the non-alcoholic fatty liver disease.
The invention also provides a diagnostic device, which can be used for detecting the non-alcoholic fatty liver disease state of a subject and comprehensively, quickly and accurately grading the state and the progress of the non-alcoholic fatty liver disease by detecting the level of the biomarker combination, can be used for assisting in monitoring the treatment effect in the diagnosis, auxiliary diagnosis and treatment processes of the non-alcoholic fatty liver disease, and can be used for evaluating the treatment effect of a treatment medicament for treating the non-alcoholic fatty liver disease.
The invention also provides a scheme of using the computer system as a diagnosis device for evaluating the nonalcoholic fatty liver disease state of the test sample.
The applicant has conducted long-term and intensive research in the field of biomarker diagnosis of liver diseases and has continuously obtained breakthrough research results. Prior patent applications such as patent application No. CN201780003854.7 filed on 29.5.2017, with the patent name "liver disease-related biomarkers and methods of use and related applications", provided a variety of biomarkers useful in the detection of patients with different states of liver disease, such as NASH, liver fibrosis and cirrhosis, and prior application with patent application No. CN201810769995.6 filed on 13.7.2018, with the patent name "biomarkers and kits for liver fibrosis and cirrhosis diagnosis and applications thereof", provided a combination of elaidic acid, taurocholic acid, tyrosine and valine or a combination of taurocholic acid and tyrosine, and optionally included other biomarkers for liver fibrosis detection.
In the above studies, the applicant found that the pathogenesis and the course of the disease of NAFLD are very complex, and the pathogenesis derived from NASH in the course of the disease is not clear, but the occurrence of the pathogenesis is a key step of further deterioration of NAFLD, and it is very important to take appropriate treatment measures in time. If NAFLD and the stage or state of the disease course can be detected quickly, comprehensively and accurately, it will have very important meaning. Therefore, the applicant carries out more intensive research on the activity of NAFLD and the development stage of NAFLD, and finds that the diagnosis of NAFLD and the accurate and rapid detection of each stage can be realized by adopting the diagnosis index provided by the invention. These diagnostic indicators include clinical indicators that are available with clinical routine testing and/or levels of biomarkers that can be obtained based on routine testing. Based on the concept, method and data provided by the invention, comprehensive, accurate, rapid and comprehensive grading diagnosis of the non-alcoholic fatty liver disease state becomes possible.
In order to specifically explain the present invention, some terms involved in the present invention are first explained below:
the non-alcoholic fatty liver disease (NAFLD) in the invention refers to a clinical pathological syndrome which is mainly characterized by excessive fat deposition in liver cells caused by removing alcohol and other definite liver damage factors, and acquired metabolic stress liver injury closely related to insulin resistance and genetic susceptibility. It is believed that non-alcoholic steatohepatitis may manifest as different courses, different stages and corresponding different symptoms of simple fatty liver, steatohepatitis (NASH) and cirrhosis, depending on the different stages or courses.
The term "non-alcoholic fatty liver disease state" as used herein refers to the state of differentiation between different stages, progression and symptoms of the progression of non-alcoholic fatty liver disease, and the non-alcoholic fatty liver disease state may include a healthy state, i.e., a state without non-alcoholic fatty liver disease, simple non-alcoholic fatty liver disease and its different stages, non-alcoholic steatohepatitis, and different stages of non-alcoholic steatohepatitis accompanied by fibrosis.
Specifically, non-alcoholic fatty liver disease without fibrosis is also called simple non-alcoholic fatty liver, and is classified into S0-S3 stage according to the degree of fatty degeneration, wherein S0 refers to no fatty degeneration, and S1 refers to mild fatty liver; s2 refers to moderate fatty liver; s3 refers to severe fatty liver.
Non-alcoholic steatohepatitis and post-fibrotic stages can be classified as S0-S4, where S0 can be considered to be the absence of liver fibrosis (including substantial absence or minimal presence); stage S1 may be considered to be the presence of only (or only substantial presence of) mild fibrosis or fibrosis in the portal region (portal fibrosis); stage S2 may be considered to be characterized by moderate fibrosis, or fibrosis between portal regions, or portal separation but still having intact hepatic structure, or fibrosis without substantial damage to lobular structure; stage S3 may be characterized by severe fibrosis, or fibrotic bridging between portal regions and central veins, or structural deformation without significant cirrhosis; stage S4 may be characterized by cirrhosis, or the formation of pseudolobules or changes in liver structure.
Although the samples used in the present invention have been described with respect to the definition of non-alcoholic fatty liver disease states, such as the predefined classification of liver fibrosis (e.g., stage S0-S4), these classifications are colloquially classically classified by way of a liver punch biopsy. Since nonalcoholic fatty liver disease is a dynamic and progressive process, the present invention can selectively define a new classification of nonalcoholic fatty liver disease based on changes in diagnostic marker levels (e.g., by model scoring), which may not be identical to biopsy-based stage classification. For example, fibrosis can be selectively divided into two stages, for example, mild fibrosis (e.g., stages S0-S2) and severe fibrosis (e.g., stages S3-S4). Alternatively, fibrosis can be selectively stratified into three stages, for example, early stage fibrosis (e.g., stages S0-S1), intermediate stage fibrosis (e.g., stage S2), and late stage fibrosis (e.g., stages S3-S4). Alternatively, fibrosis may be classified by a model into 4 or more discrete stages, or using a continuous classification model (e.g., a higher score indicates a higher degree of fibrosis).
For convenience, the "diagnostic device" of the present invention may include a kit, a medical apparatus, a detection device, and other suitable product forms capable of achieving the object of the present invention, and is not strictly defined under the relevant medical product registration regulations. The invention comprises a plurality of detection indexes, and can select a proper product form. For example, when a biomarker contained in a biological sample of a subject is selected or included as a detection target, the detection target may be a kit containing a diagnostic model and a quantitative detection reagent for the biomarker as described below, or may be an integrated diagnostic device or detection apparatus containing a quantitative detection apparatus and a diagnostic model.
The diagnosis model refers to a statistical model with diagnosis capability obtained by training a statistical model according to the concept and the method of the invention on the basis of a certain sample; preferably, the method preferably trains a random forest model and a gradient lifting tree model to obtain a statistical model with diagnosis capability.
According to the sample data, the screening index, the screening method and the selected statistical model, a corresponding diagnosis model can be obtained and applied to the purpose of the invention; the diagnostic model may also exist in other forms or products, such as an excel file, or a suite of executable computer programs, as long as the underlying logic is based on the inventive concepts and methods.
In the present invention, the following software products are exemplarily provided for the establishment of the diagnostic model of the present invention, but are not limited thereto:
the random forest model can select LiveForest software of Shenzhen, painted cloud, biotechnology, Limited, which has the copyright registration number 2018SR227394, software name: a chronic liver disease machine learning diagnosis system V1.0 based on metabonomics.
The gradient promotion tree model can be selected from LiveBoost software of Shenzhen, cloud biotechnology and Limited, the copyright registration number of the LiveBoost software is 2018SR528279, the name of the software is: chronic liver disease risk calculation software V1.0; and LiveFat software, profanity, cloud biotechnology, ltd, and software copyright registration No. 2020SR0484209, software name: liver steatosis risk calculation software V1.0.
As used herein, a "biomarker" refers to a unique biological or biologically derived indicator of a process, event or disorder. Biomarkers can be used for risk assessment, status assessment, and diagnosis, such as assessment of the high or low risk of developing a certain disease, assessment of the disease status, clinical diagnosis, prognostic assessment, and monitoring of therapy outcome. They can also be used to identify patients most likely to respond to a particular treatment, for drug screening and for drug development. Thus, biomarkers can be used for clinical diagnosis and assessment of disease, and are also valuable for identifying new drug therapies and finding new targets for drug therapies. They are also valuable for exploring dosage regimens and drug combinations.
In the present invention, the "subject" includes all mammals, and as one embodiment of the present invention, the subject is a human.
In the present invention, the "biological sample" refers to plasma, serum, feces or saliva derived from a subject, and in the present invention, peripheral venous blood, plasma or serum derived from a subject is selected as an example.
The invention achieves the purpose through the following technical scheme:
the applicant carries out deep research on the NAFLD activity and the NAFLD development stage, and finds that the diagnosis of NAFLD and the accurate and rapid detection of each stage can be realized by adopting the diagnosis index provided by the invention. These diagnostic indicators include clinical indicators that are available with clinically routine testing. Applicants have also discovered that the use of certain biomarkers obtained based on conventional assays can also be used as diagnostic indicators for the purposes of the present invention to conduct the detection of non-alcoholic fatty liver disease states. These clinical markers and biomarkers can be used alone or in combination as diagnostic markers.
The research results provide convenient, quick and reliable detection means and related products for clinical screening of the non-alcoholic fatty liver disease, diagnosis of different states of the non-alcoholic fatty liver disease and monitoring of the treatment effect in the treatment process of the non-alcoholic fatty liver disease. The present invention provides a biomarker combination useful for detecting a non-alcoholic fatty liver disease state in a subject, the biomarker combination comprising hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate in a biological sample from the subject, the biomarker combination optionally further comprising or excluding one or more of the following biomarkers: glycohyocholic acid, glycohyodeoxycholic acid, taurolicholic acid, taurolihyodeoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurolichenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid and succinic acid.
The biomarker combinations described above in the present invention may optionally be used for the purposes of the present invention, either alone or in combination with clinical indicators including, as one of the examples, the sex of the subject and one or more of glycated hemoglobin, alanine aminotransferase, blood triglycerides, low density lipoprotein, apolipoprotein a, apolipoprotein E, bound bilirubin, uric acid, serum iron-binding unsaturated iron, and serum total iron-binding of a biological sample from the subject.
As another example, in combination with clinical criteria, the clinical criteria include the age, body mass index, fasting plasma glucose, gamma glutamyl transferase, high density lipoprotein, cholesterol, total bilirubin, albumin, aspartate aminotransferase, and platelet count of the subject; and further optionally including one or more of the sex of the subject and glycated hemoglobin, alanine aminotransferase, blood triglycerides, low density lipoprotein, apolipoprotein a, apolipoprotein E, conjugated bilirubin, uric acid, serum iron, serum unsaturated iron binding capacity, and serum total iron binding capacity of a biological sample of the subject.
The invention also provides a kit for quantitatively detecting the biomarker combination, wherein the kit comprises a biological sample extraction liquid and a quantitative detection reagent for the biomarkers. The quantitative detection reagent comprises a standard solution of the biomarker, an internal standard solution and an extraction liquid, wherein the internal standard solution refers to the isotope-labeled biomarker, and the isotope-labeling mode is selected from 2H and 13C; the biological sample extract liquid consists of methanol and acetonitrile, and the volume ratio of the methanol to the acetonitrile is as follows: 1: 1-5: 1. as one embodiment, the kit may further comprise a 96-well plate of 700. mu.L, a V-shaped 96-well plate of 350. mu.L, a plate-sealed silica gel, and a 96-well sealing aluminum film for convenience of use.
The method for using the kit of the invention comprises the steps of determining the content of the biomarker in a biological sample of a subject for evaluating the non-alcoholic fatty liver state of the subject; when used for such assessment purposes, optionally in combination with or without clinical markers as described above; when the method is used for the above evaluation purpose, these values are input to a random forest model or a gradient enhanced tree model with a preset cutoff value, and the evaluation of the non-alcoholic fatty liver disease state of the subject is performed by comparing the values with the cutoff value.
Determining a biomarker in serum or plasma of a subject comprising the steps of:
a) preparing standard substance solutions of biomarkers with different concentrations;
b) preparing an internal standard solution;
c) preparing a serum or plasma sample of a subject;
d) measuring the sample prepared in c) by liquid chromatography and mass spectrometry, and calculating the concentration of the metabolite in the sample.
In some embodiments, the biological sample is treated by adding the biological sample to a precipitating agent selected from the group consisting of a mixed solvent of isopropanol and methanol; in some embodiments, the precipitating agent is a mixed solvent of isopropanol and methanol; the volume ratio may be 1: 1-1: 10, preferably, the volume ratio may be 1: 3-1: 5.
the liquid chromatography is selected from high performance liquid chromatography, ultra high performance liquid chromatography and nanoliter liquid chromatography. In some embodiments, the liquid phase conditions may be: mobile phase a was 70% methanol +5 mmol ammonium formate, mobile phase B was 90% isopropanol + 10% methanol +5 mmol ammonium formate, the column was a 100 mm C18 column with a column temperature set at 40 ℃ and a flow rate of 0.3 ml/min, 40% mobile phase B was maintained for 0-0.5 min, 0.5-2 min was linearly changed from 40% B to 70% mobile phase B, 2-4.0 min was linearly changed from 70% B to 80% mobile phase B, 4.0-4.5 min was linearly changed from 80% mobile phase B to 98% mobile phase B, 4.5-6 min was maintained for 98% mobile phase.
The mass spectrum comprises a quadrupole mass spectrum, a time-of-flight mass spectrum, an ion hydrazine mass spectrum and a high-resolution orbital hydrazine mass spectrum. In some embodiments, the mass spectrometry conditions are data acquisition using a triple quadrupole mass spectrometry multiple reaction monitoring mode, selecting characteristic ion pair information for the diagnostic marker composition, and establishing an information confirmation and detection method using a standard, while performing quantitative calibration using an internal standard to obtain an accurate concentration value and a related ratio value for each diagnostic marker composition in the sample.
The invention also provides application of the biomarker combination in preparation of a product for screening non-alcoholic fatty liver disease, non-alcoholic steatohepatitis and non-alcoholic steatohepatitis with fibrosis and evaluating the treatment effect of the non-alcoholic steatohepatitis and the fibrosis.
The biomarker combination can be used for screening and evaluating the treatment effect of liver disease treatment drugs, for example, screening and evaluating the liver disease treatment drugs by monitoring the content of the biomarker combination in a test body.
The biomarker combination can be used for preparing products for screening or evaluating treatment drugs for non-alcoholic fatty liver diseases, wherein the non-alcoholic fatty liver diseases comprise simple non-alcoholic fatty liver and different stages thereof, non-alcoholic steatohepatitis and different stages of non-alcoholic steatohepatitis accompanied with fibrosis; the different stages of the non-alcoholic steatohepatitis accompanied with fibrosis comprise early stage hepatic fibrosis of the non-alcoholic steatohepatitis (S0-2), late stage hepatic fibrosis accompanied with the non-alcoholic steatohepatitis (S3-4) and liver cirrhosis accompanied with the non-alcoholic steatohepatitis (S4). In particular, by determining the amount of the biomarker combination in a human or other mammal and comparing to the amount of the biomarker in a healthy state, it is useful to diagnose the state of non-alcoholic fatty liver disease, such as non-alcoholic steatohepatitis (NASH), and to determine the activity, stage or severity of non-alcoholic fatty liver disease in a subject, or to classify a subject as a recipient or non-recipient of a non-alcoholic fatty liver disease treatment, or to assess the efficacy of a related drug treatment, or to determine the development or regression of a pathology in a non-alcoholic fatty liver disease patient, or to classify a patient as a potential responder or non-responder to a medical treatment, or to predict the disease outcome of a patient.
The invention also provides the application of the biomarker combination in preparing a diagnostic device for detecting the non-alcoholic fatty liver disease state of a subject, and the diagnostic index of the diagnostic device comprises the biomarker.
The diagnosis device can be used for detecting the nonalcoholic fatty liver disease state of a subject.
To achieve this, the diagnostic device may include a diagnostic index input module and a non-alcoholic fatty liver disease state assessment module; wherein the diagnostic indicator input module is at least to: obtaining a diagnostic index of a subject, the diagnostic index comprising a protocol as described above; the non-alcoholic fatty liver disease state assessment module is to perform at least the following: inputting the level of the diagnosis index acquired by the diagnosis index input module into a diagnosis model to obtain a score value; and comparing the obtained score value with a preset cutoff value of a diagnosis model, and outputting an evaluation result of the non-alcoholic fatty liver disease state of the test subject.
As an embodiment, the diagnostic indicator comprises the biomarker combination; the biomarker combination comprises hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate in a biological sample of a subject, and optionally further comprises or does not comprise one or more of the following biomarkers: glycohyocholic acid, glycohyodeoxycholic acid, taurolicholic acid, taurolihyodeoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurolichenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid and succinic acid.
As an embodiment, the diagnostic index may further combine clinical indexes on the basis of the biomarkers in addition to the biomarker combinations described above; as one example of this embodiment, the clinical indicators include the age, body mass index, fasting plasma glucose, gamma glutamyl transferase, high density lipoprotein, cholesterol, total bilirubin, albumin, aspartate aminotransferase, and platelet count of the subject; and further optionally including the sex of the subject and one or more of glycated hemoglobin, alanine aminotransferase, blood triglycerides, low density lipoprotein, apolipoprotein a, apolipoprotein E, conjugated bilirubin, uric acid, serum iron-unsaturated binding capacity, and serum total iron-binding capacity of a biological sample from the subject.
As another example, the clinical criteria may include one or more of bound bilirubin, uric acid, serum iron-unsaturation, and serum total iron-binding of a biological sample from the subject.
The diagnostic model is selected from a random forest model and a gradient lifting tree model.
The subject is a human, and the biological sample is blood, plasma, or serum from the subject.
The diagnostic device further comprises a biological sample detection module; for performing biological sample pre-treatment and quantitative detection, particularly when these diagnostic indicators are derived from biological samples. The detection module can be a liquid chromatography-mass spectrometry combination. The liquid chromatography is selected from high performance liquid chromatography, ultra high performance liquid chromatography and nanoliter liquid chromatography. In some embodiments, the liquid phase conditions may be: mobile phase a was 70% methanol +5 mmol ammonium formate, mobile phase B was 90% isopropanol + 10% methanol +5 mmol ammonium formate, the column was a 100 mm C18 column with a column temperature set at 40 ℃ and a flow rate of 0.3 ml/min, 40% mobile phase B was maintained for 0-0.5 min, 0.5-2 min was linearly changed from 40% B to 70% mobile phase B, 2-4.0 min was linearly changed from 70% B to 80% mobile phase B, 4.0-4.5 min was linearly changed from 80% mobile phase B to 98% mobile phase B, 4.5-6 min was maintained for 98% mobile phase. The mass spectrum comprises a quadrupole mass spectrum, a time-of-flight mass spectrum, an ion hydrazine mass spectrum and a high-resolution orbital hydrazine mass spectrum. In some embodiments, the mass spectrometry conditions are data acquisition using a triple quadrupole mass spectrometry multiple reaction monitoring mode, selecting characteristic ion pair information for the diagnostic marker composition, and establishing an information confirmation and detection method using a standard, while performing quantitative calibration using an internal standard to obtain an accurate concentration value and a related ratio value for each biomarker in the sample.
The diagnostic device of the present invention has many options in physical form and product form, and may be a kit, a medical instrument, and a detection device. In these product forms, the diagnostic model may be in the form of software, e.g., as part of a kit, assembled in the kit; or in the form of a computer program, in an integrated or non-integrated diagnostic device or detection apparatus.
As an exemplary embodiment, the diagnostic device of the present invention may be an integrated device integrating modules, for example, a diagnostic index input module and a non-alcoholic fatty liver disease state evaluation module, or may further include a biological sample detection module; specifically, the diagnostic index input module and the non-alcoholic fatty liver disease state evaluation module are stored in a computer in the form of computer programs, and are connected with an instrument of the biological sample detection module to realize data transmission, so as to form an integrated device, such as an integrated diagnostic device of the computer, the high performance liquid chromatography and the mass spectrum.
As an exemplary embodiment, the diagnostic device of the present invention may be physically separated modules, for example, a diagnostic index may be obtained by other conventional means, and the diagnostic index may be input to a computer storing a diagnostic index input module and a non-alcoholic fatty liver disease state evaluation module.
Computer system
The invention also provides a computer system for evaluating the non-alcoholic fatty liver disease state of a subject, which is one of the schemes of the diagnosis device, wherein the computer system comprises a diagnosis index input module and a non-alcoholic fatty liver disease state evaluation module; wherein the diagnostic indicator input module is at least to: obtaining a diagnosis index of a test body;
the non-alcoholic fatty liver disease state assessment module is at least to perform the following: inputting the level of the diagnosis index acquired by the diagnosis index input module into a diagnosis model to obtain a score value; comparing the obtained score value with a preset cutoff value of a diagnosis model, and outputting an evaluation result of the non-alcoholic fatty liver disease state of the test subject;
the non-alcoholic fatty liver disease state comprises a non-alcoholic liver disease related state preset by a diagnosis model.
The diagnosis index in the diagnosis device is also applicable to the computer system.
The diagnostic model is selected from a random forest model and a gradient lifting tree model.
Through the technical scheme, the method realizes accurate, quick and comprehensive evaluation and diagnosis of the non-alcoholic fatty liver disease and the state. In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the following detailed description of the embodiments of the present invention with reference to the drawings is provided for further explaining the substantive content of the present invention, but the content and the protection scope of the present invention are not limited by the embodiments. Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the true spirit and scope of the present invention.
Drawings
FIG. 1 is a flow chart of algorithm training
FIG. 2 is a ROC curve of the biomarker gradient elevation tree model for differentiating patients with early stage hepatic fibrosis (S0-2) and late stage hepatic fibrosis (S3-4) and patients with late stage hepatic fibrosis (S4)
FIG. 3 is a recall curve of the biomarker gradient-boosted tree model for distinguishing patients with early stage hepatic fibrosis (S0-2) and late stage hepatic fibrosis (S3-4) and patients with late stage hepatic fibrosis (S4)
FIG. 4 is risk factor scores for a model for biomarker combinations in a training set for diagnostic prediction
FIG. 5 is risk factor score for a model of biomarker combinations in a test set for diagnostic prediction
FIG. 6 is a ROC curve for a biomarker random forest model to differentiate healthy controls from non-alcoholic fatty liver disease
FIG. 7 is a ROC curve for a random forest model of biomarkers to distinguish simple fatty liver disease patients from steatohepatitis patients
FIG. 8 is a ROC curve for a biomarker random forest model to differentiate healthy controls from non-alcoholic fatty liver disease
FIG. 9 is a ROC curve for a random forest model of biomarkers to distinguish simple fatty liver disease patients from steatohepatitis patients
FIG. 10 is a ROC curve of biomarker gradient spanning tree model for differentiating simple fatty liver disease states
FIG. 11 is a ROC curve for biomarker and clinical index gradient-boosted tree model to discriminate healthy controls from non-alcoholic fatty liver disease, and from simple fatty liver disease and steatohepatitis patients
FIG. 12 is a ROC curve of the biomarker and clinical index gradient Tree model for differentiating patients with early stage hepatic fibrosis (S0-2) from patients with late stage hepatic fibrosis (S3-4) and differentiating patients with late stage hepatic fibrosis (S4)
FIG. 13 is a graph of recall curves for biomarker and clinical index gradient boosted tree models to differentiate patients with early stage liver fibrosis (S0-2) from patients with late stage liver fibrosis (S3-4) and to differentiate patients with late stage liver fibrosis (S4)
Detailed Description
The following examples describe in detail the diagnostic index, diagnostic model, biomarker panel, diagnostic device, and the accuracy, rapidity, and comprehensiveness of using them to diagnose non-alcoholic fatty liver disease in accordance with the present invention.
EXAMPLE 1 study sample
The experimental samples and methods relevant to the present invention are as follows:
(I) clinical samples
The clinical trial is a multicenter experiment, and the trial samples of the present invention were approved by the local ethical committee and informed consent was obtained from all subjects. The liver specimen is from an adult patient who receives liver biopsy and meets the clinical diagnosis standard of NAFLD, NASH and the like; all subjects with the following conditions were excluded from the study: history of alcohol consumption, cancer, diabetes or any other disease associated with the liver. Peripheral venous blood samples were taken from these subjects after 12 hours overnight fast.
According to the study protocol, the following analyses were performed on all samples:
hematology includes hemoglobin, hematocrit, RBC count, white blood cells, differential white blood cell count (neutrophils, lymphocytes, eosinophils, monocytes, basophils-absolute and% values), platelets, and reticulocytes.
Biochemical group I includes plasma glucose, Triglycerides (TG), creatinine clearance, gamma-glutamyltransferase (GGT), aspartate Aminotransferase (AST), alanine Aminotransferase (ALT), Creatine Phosphokinase (CPK), alkaline phosphatase, Thyroid Stimulating Hormone (TSH) and HbA1 c.
Biochemical group II includes plasma glucose, creatinine clearance, total protein, albumin, sodium, potassium, chloride, calcium, uric acid, urea expressed as Blood Urea Nitrogen (BUN), aspartate Aminotransferase (AST), alanine Aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase, Creatine Phosphokinase (CPK), total bilirubin, conjugated bilirubin, reactive protein type C (hsCRP), AST/ALT ratio, and HbA 1C.
The lipidome comprises Triglycerides (TG), total cholesterol, non-HDL-C (calculated), High Density Lipoprotein Cholesterol (HDLC), low density lipoprotein (LDL-C) (calculated), calculated very low density lipoprotein cholesterol (VLDL-C) (calculated), apolipoprotein AI (ApoAI) and apolipoprotein B (ApoB).
The main clinical information is shown in tables 1A, 1B and 2 below:
TABLE 1A clinical information sheet of non-alcoholic fatty liver fibrosis sample (training set)
TABLE 1B clinical information sheet of non-alcoholic fatty liver fibrosis sample (test set)
TABLE 2 simple fatty liver patient clinical information sheet
(II) biological sample Collection
The biological samples used in the present invention were from clinical samples prior to the treatment period of example 1. Written informed consent was obtained from each clinical specimen (patient) participating in the clinical trial to collect, store and use additional samples. Fasting (at least 12 hours) venous blood was collected via the median cubital vein using a clinical EDTA anticoagulated vacuum blood collection tube. And plasma was separated within 1 hour after collection (centrifugation parameters: 1000 g. times.15 min). The plasma was filled into a 1.5mL centrifuge tube, the permanent marker marked the sample tube, placed into a cryopreservation box, quickly stored at-80 ℃ for future use and made information registration. Detecting corresponding indexes according to a clinical routine method. And carrying out full-spectrum content measurement on the metabolites in the biological sample.
EXAMPLE 2 diagnostic modeling
And (3) training by taking 70% of training set data as a screening algorithm, and taking 30% of training set data as algorithm test evaluation. In order to fully and reasonably compare the classification performance of the algorithm, training data are randomly divided 100 times according to the ratio of 7:3, 100 times of modeling prediction is carried out by respectively using a Decision Tree (DT), a Random Forest (RF) and a gradient boosting tree (GBDT, which is referred to as a GB model for short), and the algorithm training flow chart is shown in figure 1.
Evaluation index and method: the area under the working characteristic curve (AUROC) of a subject can be selected to make a judgment index for measuring the classification performance of the classifier, and the model design algorithm with the maximum AUROC and the maximum mean value of 100 predictions in four algorithm models is selected as the optimum model.
The method uses a machine learning algorithm to establish a random forest model and a gradient lifting tree model as diagnosis models, can output the non-alcoholic fatty liver disease state of a subject, and finds the optimal cutoff value through the optimal point of logging in ROC analysis.
Example 3 biomarker combination diagnostic model
The levels of the metabolites determined in example 1 and the non-alcoholic fatty liver disease state in the clinical sample were modeled using the machine learning algorithm of the above example. Training a random forest model or a gradient lifting tree model in a training set, wherein the biomarker with diagnostic ability comprises hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate, and can be further combined with one or more of glycohyocholic acid, glycohyodeoxycholic acid, taurocholic hyodeoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurochenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid and succinic acid, inputting the biomarker into a diagnostic model for calculation, and can be further combined with one or more of age, platelet count, aspartate aminotransferase, serum unsaturated iron binding force and serum total iron binding force level, inputting a diagnosis model for calculation; and hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate can also be input into a diagnostic model for calculation in combination with one or more of age, platelet count, aspartate aminotransferase, serum iron-binding unsaturation, and serum total iron-binding levels.
By measuring the biomarker combination, whether the subject has the nonalcoholic fatty liver disease and the state can be distinguished through a diagnosis model, and an optimal cutoff value is found through a about-optimal point in ROC analysis.
Example 4 quantitative detection of biomarker combinations
The quantitative determination method and the steps of the biomarker combination are as follows:
a) preparing biomarker standard substance solutions 1 to 7 with different concentrations, placing the biomarker standard substance solutions and a blank control in a centrifuge tube, and centrifuging for 10 to 30 minutes at 4000 and 10000 revolutions of a desktop centrifuge; adding 200 microliters of freshly prepared deionized water into each centrifugal tube, oscillating vigorously, dissolving for 10-15 minutes at 800-;
b) preparing an internal standard substance solution: taking 3 ml of methanol as an internal standard diluent, adding the internal standard solution, covering a cover, violently shaking, and standing for about 15 minutes for dissolution. Diluting the internal standard solution, and adding the internal standard solution into a 96-hole microporous plate;
c) preparation of serum or plasma samples: and taking out the 700 microliter microporous plate provided by the kit, sequentially adding 5 microliter of the standard solution 1 to the standard solution 7 and a blank control into the A1 to A8 wells, and adding 5 microliter of a serum (or plasma) sample or 5 microliter of low, medium and high concentration quality control substances into other wells. Add 25. mu.l of internal standard solution to each well, cover with a silica gel cap, and shake at 1000rpm for 10 min. Centrifuge at 2000g for 2 minutes. The silica gel cover is lightly taken down to avoid liquid in the micropore plate from splashing, and the silica gel pad is properly placed to prevent pollution for later use; reacting at 30 ℃ and 1450rpm for 60 minutes, carefully taking off the silica gel pad, and properly placing the silica gel pad to prevent pollution for later use; adding 350 microliters of sample loading buffer solution into each hole, and covering a silica gel cover for violent oscillation; standing at-20 deg.C for 20 min, and centrifuging at 2000g for 20 min; taking down the silica gel cover, carefully absorbing 150 microliters of supernatant into a clean V-shaped bottom microporous plate, covering an aluminum foil envelope, and putting into an automatic sample injector;
d) measuring the sample prepared in c) by liquid chromatography and mass spectrometry, and calculating the concentration of the metabolite in the sample;
liquid chromatography conditions: mobile phase a was 70% methanol +5 mmol ammonium formate, mobile phase B was 90% isopropanol + 10% methanol +5 mmol ammonium formate, the column was a 100 mm C18 column with a column temperature set at 40 ℃ and a flow rate of 0.3 ml/min, 40% mobile phase B was maintained for 0-0.5 min, 0.5-2 min was linearly changed from 40% B to 70% mobile phase B, 2-4.0 min was linearly changed from 70% B to 80% mobile phase B, 4.0-4.5 min was linearly changed from 80% mobile phase B to 98% mobile phase B, 4.5-6 min was maintained for 98% mobile phase.
The mass spectrum conditions are that a triple quadrupole mass spectrum multi-reaction monitoring mode is adopted for data acquisition, characteristic ion pair information of the markers is selected, a standard substance is adopted for information confirmation and detection method establishment, and meanwhile, an internal standard substance is adopted for quantitative correction to obtain accurate concentration values and related proportion values of all the markers in the sample.
The above assay methods are applicable to biomarker combinations of hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate and biomarker combinations further comprising one or more of glycohyocholic acid, glycohyodeoxycholic acid, taurocholic acid, tauroshyodeoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurochenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid, and succinic acid.
Example 5 biomarker gradient boosting Tree model
The levels of the metabolites hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate in the serum of clinical samples were determined according to the method and diagnostic model described in example 3, and these values were input to a gradient elevated tree model for determination. The results are as follows:
FIG. 2 shows the training set results, which distinguish early stage liver fibrosis target serum (S0-2) from late stage liver fibrosis patients (S3-4) and distinguish late stage liver fibrosis patients (S4), wherein the upper curve is the training set curve.
FIG. 3 shows the results of the test set, wherein the upper curve is the training set curve, distinguishing early stage liver fibrosis target serum (S0-2) from patients with advanced liver fibrosis (S3-4) and distinguishing patients with advanced liver fibrosis (S4). .
Fig. 4 and 5 are risk factor scores for models where biomarker combinations are used for diagnostic prediction in the training set and test set. A gradient-boosted tree model comprising the biomarker combination of the invention for differentiating between liver fibrosis target serum (S0-3) and serum of a cirrhosis patient (S4) is illustrated. The biomarker combinations in the training set and test set are used to diagnose risk factor scores for the predicted model.
The biomarker combinations can also be input into a diagnostic model for calculation in combination with age, platelet count, aspartate aminotransferase, serum iron-unsaturation binding capacity and serum total iron binding capacity.
Example 6: random forest model of biomarkers
Determining the levels of metabolites hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate in serum of clinical samples according to the method and diagnostic model described in example 3, inputting these values into a random forest model for determination, outputting the probability of the subject suffering from non-alcoholic fatty liver disease, and finding an optimal cutoff value by using the optimal point in ROC analysis, wherein the cutoff value is 0.91 when distinguishing healthy controls from non-alcoholic fatty liver disease; the cutoff value was 0.63 when distinguishing a simple fatty liver disease patient from a steatohepatitis patient.
The test set samples were tested and when differentiating between healthy controls and non-alcoholic fatty liver disease, the resultant ROC curve area and 95% confidence interval were 0.962(0.974-0.951) with sensitivity and specificity percentages of 92.3% and 90.4%, respectively (fig. 6). In distinguishing between simple fatty liver disease and steatohepatitis patients, the area under the ROC curve and the 95% confidence interval were 0.892(0.828-0.955), and the sensitivity and specificity percentages were 77.9% and 90.9%, respectively (fig. 7).
Example 7: random forest model of biomarkers
According to the method and the diagnosis model described in the previous embodiment 3, clinical samples of hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate, combined with the age, BMI, platelets, AST, serum unsaturated iron binding capacity, and serum total iron binding capacity of the subject were measured as clinical detection indexes, and the cut-off value was 0.113 in the case of distinguishing healthy controls from non-alcoholic fatty liver disease and 0.25 in the case of distinguishing simple fatty liver disease from fatty liver disease patients, using the random forest model. The test set data was tested and when differentiating between healthy controls and non-alcoholic fatty liver disease, the ROC curve area and 95% confidence interval were 0.995(0.993-1) with sensitivity and specificity percentages of 98.3% and 97.9%, respectively (fig. 8). In distinguishing between simple fatty liver disease and steatohepatitis patients, the area under the ROC curve and the 95% confidence interval were 0.95(0.928-0.985), and the sensitivity and specificity percentages were 88.4% and 95.3%, respectively (fig. 9).
Example 8 biomarker gradient boosting Tree model
According to the method and the diagnosis model described in the embodiment 3, the levels of metabolites of hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate in the serum of 123 clinical non-alcoholic fatty liver disease patients who have been confirmed by liver biopsy are measured, and these values are input into a gradient elevated tree model for determination, so as to output the possibility of the non-alcoholic fatty liver disease of the subject.
The results show that the sensitivity of diagnosing the fatty liver is 89.1 percent, the specificity is 97.3 percent, and the area under the AUC curve is 0.961; compared with the diagnosis result of a gold standard, the sensitivity of distinguishing the healthy subject from the mild fatty liver S1 is 77.1%, the specificity is 78.3%, and the area under the AUC curve is 0.797; the diagnostic sensitivity for distinguishing the severe fatty liver from the moderate fatty liver was 75.8%, the specificity was 79.7%, and the AUC was 0.803. See fig. 10.
Example 9 establishment of a diagnostic model with a combination of clinical markers and biomarkers as diagnostic markers
The machine learning algorithm of example 2 was used to quantitatively determine the levels of metabolites that may be associated with non-alcoholic fatty liver disease in the clinical sample of example 1, and the clinical indicators (age of subject, body mass index, fasting plasma glucose, gamma-glutamyltransferase, high density lipoprotein cholesterol, total bilirubin, albumin, aspartate aminotransferase, platelet count) were correlated with the non-alcoholic fatty liver disease state in the clinical sample to train a random forest model or a gradient elevation tree model in the training set. The results show that, when used alone and in combination with the above clinical criteria, biomarkers with diagnostic capabilities include hyocholic acid, hyodeoxycholic acid, taurocholic acid, and sphingosine 1-phosphate. The biomarkers can further combine one or more of age, platelet count, aspartate aminotransferase, serum unsaturated iron binding capacity and serum total iron binding capacity level, input the results into a diagnosis model for calculation, distinguish whether a subject has nonalcoholic fatty liver disease and the state of the subject through the diagnosis model, and find an optimal cut-off value through an approximate optimal point in ROC analysis.
Example 10 clinical index and biomarker combination gradient Lift Tree model to differentiate healthy controls from non-alcoholic fatty liver disease, to differentiate simple fatty liver disease and steatohepatitis patients
The method comprises the steps of analyzing 9 clinical indexes of age, body mass index, fasting blood glucose, gamma-glutamyltransferase, high-density lipoprotein cholesterol, total bilirubin, albumin, aspartate aminotransferase and platelet count of a test set subject by using a gradient lifting tree model trained in a training set, combining the levels of biomarkers such as hyocholic acid, hyodesoxycholic acid, taurocholic acid and sphingosine 1-phosphate, and inputting the levels into the gradient lifting tree model obtained by the training set for verification.
The results show that when healthy controls and non-alcoholic fatty liver disease were distinguished, the area under the ROC curve and 95% confidence interval were 0.97 for the results, and the sensitivity and specificity percentages were 93.8% and 95%, respectively, see fig. 11A. In distinguishing between simple fatty liver disease and steatohepatitis patients, the area under the ROC curve and the 95% confidence interval were 1.000, and the sensitivity and specificity percentages were 99.1% and 97.9%, respectively, as shown in fig. 11B. In fig. 11, the upper curve is the diagnostic curve of the present embodiment.
Example 11 clinical index and biomarker combination gradient Tree model to differentiate patients with early stage hepatic fibrosis (S0-2) and patients with late stage hepatic fibrosis (S3-4) and patients with late stage hepatic fibrosis (S4)
Analyzing 9 clinical indexes of age, body mass index, fasting blood glucose, gamma-glutamyltransferase, high density lipoprotein cholesterol, total bilirubin, albumin, aspartate aminotransferase and platelet count of a test set subject by using a gradient lifting tree model trained in a training set, combining the levels of biomarkers, namely hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate, and inputting the levels into the gradient lifting tree model obtained by the training set for verification.
The results showed that the area under the ROC curve was 0.924 and the sensitivity and specificity percentages were 89.8% and 97.2%, respectively, when distinguishing early hepatic fibrosis from late hepatic fibrosis, and 0.936 and 97.0% and 100%, respectively, when distinguishing hepatic fibrosis from hepatic cirrhosis, see fig. 12. Recall is shown in figure 12. In fig. 12 and 13, the upper curve is the diagnostic curve according to the present embodiment.
Example 12 computer System
The embodiment provides a computer system, which comprises a diagnosis index input module and a non-alcoholic fatty liver disease state evaluation module. The diagnostic indicator input module may receive a subject diagnostic indicator input including the subject's age, body mass index, fasting glucose, gamma glutamyl transferase, high density lipoprotein, cholesterol, total bilirubin, albumin, aspartate aminotransferase, and platelet count of a biological sample of the subject. The non-alcoholic fatty liver disease state evaluation module is a random forest model or a gradient lifting tree model trained in embodiment 3, is used as a program and is arranged in a computer, and a score value can be obtained when the level of the diagnosis index acquired by the diagnosis index input module is input into the diagnosis model; and comparing the obtained score value with a preset cutoff value of a diagnosis model, and outputting an evaluation result of the non-alcoholic fatty liver disease state of the test subject.
The diagnostic indicator may be in a variety of formats, for example:
biomarker combination (I): comprising hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate in a biological sample from a subject, optionally further comprising or excluding one or more of the following biomarkers: glycohyocholic acid, glycohyodeoxycholic acid, taurolicholic acid, taurolideoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurochenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid, and succinic acid, or
(II) further combining clinical indexes on the basis of the biomarker combination; the clinical indicators comprise the age, body mass index, fasting plasma glucose, gamma-glutamyltransferase, high density lipoprotein, cholesterol, total bilirubin, albumin, aspartate aminotransferase and platelet count of a biological sample from a subject; and further optionally including one or more of the sex of the subject and glycated hemoglobin, alanine aminotransferase, blood triglycerides, low density lipoprotein, apolipoprotein A, apolipoprotein E, conjugated bilirubin, uric acid, serum iron, serum unsaturated iron binding capacity and serum total iron binding capacity of a biological sample of the subject, or
(III) the following clinical indexes are further combined on the basis of the biomarker combination: one or more of bilirubin, uric acid, serum iron-unsaturated binding capacity and serum total iron binding capacity.
The diagnostic model is selected from a random forest model and a gradient lifting tree model.
EXAMPLE 13 diagnostic device
This embodiment provides a computer including the computer system of embodiment 10 as a diagnosis apparatus. The diagnosis device can also be combined with a biological sample detection module, and the modules are integrated into an integrated device, such as an integrated diagnosis device of a computer, high performance liquid chromatography and mass spectrometry. For performing biological sample pre-treatment and quantitative detection, particularly when these diagnostic indicators are derived from biological samples. The detection module can be used by combining high performance liquid chromatography and mass spectrometry; reference is made in particular to example 4.
EXAMPLE 14 kit
This example provides a kit for detecting the biomarker combinations described in example 3, the kit comprising a biological sample extract and a quantitative detection reagent for the biomarkers. The quantitative detection reagent comprises a standard solution, an internal standard solution and an extraction liquid of the biomarker, wherein the internal standard solution refers to the biomarker marked by an isotope, and the isotope marking mode is selected from 13C; the biological sample extract liquid consists of methanol and acetonitrile, and the volume ratio of the methanol to the acetonitrile is as follows: 2: 1, the kit also comprises a 96-well plate of 700 mu L, a V-shaped 96-well plate of 350 mu L, sealing plate silica gel and a 96-well sealing aluminum film.
Claims (13)
1. A biomarker combination for detecting a non-alcoholic fatty liver disease state of a subject, comprising hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate derived from a biological sample of the subject; the non-alcoholic fatty liver disease comprises simple non-alcoholic fatty liver and different stages thereof, non-alcoholic steatohepatitis and different stages of non-alcoholic steatohepatitis accompanied with fibrosis; the different stages of the non-alcoholic steatohepatitis accompanied with fibrosis comprise early stage hepatic fibrosis of the non-alcoholic steatohepatitis (S0-2), late stage hepatic fibrosis accompanied with the non-alcoholic steatohepatitis (S3-4) and liver cirrhosis accompanied with the non-alcoholic steatohepatitis (S4).
2. The biomarker combination of claim 1, further optionally comprising one or more of the following biomarkers: glycohyocholic acid, glycohyodeoxycholic acid, taurolicholic acid, taurolihyodeoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurolichenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid and succinic acid.
3. The biomarker combination of any of claims 1 or 2, wherein the subject is a human and the biological sample is blood, plasma, or serum from the subject.
4. Use of the biomarker combination according to any one of claims 1 or 2 for the preparation of a diagnostic device for detecting a non-alcoholic fatty liver disease state of a subject, wherein the diagnostic device uses the level of the biomarker as a detection indicator.
5. Use of the biomarker combination according to claim 4 for the preparation of a diagnostic device for detecting the non-alcoholic fatty liver disease state of a subject, comprising the following steps when used for said use: determining the amount of a biomarker in a biological sample from the subject, optionally with or without the subject's age, platelet count, aspartate aminotransferase, serum iron binding unsaturation and serum total iron binding, and inputting these values into a random forest model or a gradient-boosting tree model; the biological sample is selected from blood, serum or plasma of a subject.
6. A kit comprising a combination of biomarkers according to any one of claims 1 to 2 for quantitative detection, wherein the kit comprises a biological sample extract and a quantitative detection reagent for the biomarkers; the quantitative detection reagent for the biomarkers comprises a standard solution, an internal standard solution and an extraction liquid of the biomarkers, wherein the internal standard solution refers to the biomarkers marked by isotopes in a mode selected from 2 H and 13 c; the biological sample extract liquid consists of methanol and acetonitrile, and the volume ratio of the methanol to the acetonitrile is as follows: 1: 1-5: 1.
7. use of the biomarker combination according to any one of claims 1 to 2 for the manufacture of a product for screening or evaluating a therapeutic drug for non-alcoholic fatty liver disease, including simple non-alcoholic fatty liver disease and its different stages, non-alcoholic steatohepatitis with different stages of fibrosis; the different stages of the non-alcoholic steatohepatitis accompanied with fibrosis comprise early stage hepatic fibrosis of the non-alcoholic steatohepatitis (S0-2), late stage hepatic fibrosis accompanied with the non-alcoholic steatohepatitis (S3-4) and liver cirrhosis accompanied with the non-alcoholic steatohepatitis (S4).
8. A computer system for assessing a non-alcoholic fatty liver disease state in a subject, the computer system comprising a diagnostic index input module and a non-alcoholic fatty liver disease state assessment module; wherein the diagnostic indicator input module is at least to: obtaining a diagnostic index of a test body, wherein the diagnostic index comprises hyocholic acid, hyodeoxycholic acid, taurocholic acid and sphingosine 1-phosphate which are derived from a biological sample of the test body;
the non-alcoholic fatty liver disease state assessment module is at least to perform the following: inputting the level of the diagnosis index acquired by the diagnosis index input module into a diagnosis model to obtain a score value; comparing the obtained score value with a preset cutoff value of a diagnosis model, and outputting an evaluation result of the non-alcoholic fatty liver disease state of the test subject;
the nonalcoholic fatty liver disease state comprises a nonalcoholic liver disease related state preset by a diagnosis model; the non-alcoholic steatohepatitis state comprises healthy, simple non-alcoholic fatty liver and different stages thereof, non-alcoholic steatohepatitis and different stages of non-alcoholic steatohepatitis accompanied with fibrosis; the different stages of the non-alcoholic steatohepatitis accompanied with fibrosis comprise early stage hepatic fibrosis of the non-alcoholic steatohepatitis (S0-2), late stage hepatic fibrosis accompanied with the non-alcoholic steatohepatitis (S3-4) and liver cirrhosis accompanied with the non-alcoholic steatohepatitis (S4)).
9. The computer system of claim 8, wherein the diagnostic indicator further comprises one or more of the following biomarkers: glycohyocholic acid, glycohyodeoxycholic acid, taurolicholic acid, taurolihyodeoxycholic acid, fructose, valine, phenylalanine, 4-hydroxyphenylpyruvic acid, ceramide, octadecadienoic acid, glycochenodeoxycholic acid, taurolichenodeoxycholic acid, malic acid, 4-hydroxyhippuric acid, 2-phenylpropionic acid, 2-hydroxybutyric acid and succinic acid.
10. The computer system of any one of claims 8-9, wherein the diagnostic indicators further comprise age, platelet count, aspartate aminotransferase, serum unsaturated iron binding capacity and serum total iron binding capacity of the subject.
11. A computer system as claimed in any one of claims 8 to 9, wherein the diagnostic model is selected from a random forest model and a gradient-boosted tree model.
12. The computer system of any one of claims 8 to 9, wherein the diagnostic criteria further include age, body mass index, fasting glucose, gamma-glutamyl transferase, high density lipoprotein, cholesterol, total bilirubin, albumin, aspartate aminotransferase, and platelet count of the subject.
13. The computer system of claim 12, wherein the diagnostic indicator further comprises one or more of gender of the subject and glycated hemoglobin of a biological sample of the subject, alanine aminotransferase, triglycerides, low density lipoprotein, apolipoprotein a, apolipoprotein E, conjugated bilirubin, uric acid, serum iron-binding unsaturated iron, and serum total iron-binding.
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| CN115684401A (en) * | 2022-10-27 | 2023-02-03 | 黑龙江八一农垦大学 | System for predicting ketosis fatty liver syndrome of dairy cow by using blood biochemical indexes and application |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115684401A (en) * | 2022-10-27 | 2023-02-03 | 黑龙江八一农垦大学 | System for predicting ketosis fatty liver syndrome of dairy cow by using blood biochemical indexes and application |
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