WO2022255781A1

WO2022255781A1 - Biomarker for disease activity assessment, diagnosis, and onset prediction of rheumatoid arthritis

Info

Publication number: WO2022255781A1
Application number: PCT/KR2022/007759
Authority: WO
Inventors: 김완욱; 고정희; 박영재; 권성원; 윤상준; 김민아
Original assignee: 가톨릭대학교 산학협력단; 서울대학교산학협력단
Priority date: 2021-06-02
Filing date: 2022-05-31
Publication date: 2022-12-08

Abstract

The present invention relates to lipid biomarkers for assessing disease activity of rheumatoid arthritis. It was identified in the present invention that lipid biomarkers selected through lipidomics analysis varied in expression level depending on disease activity and can be utilized to classify the disease activity of rheumatoid arthritis patients into mild and medium-high grades. In addition, the biomarkers can distinguish between osteoarthritis and active rheumatoid arthritis and have changed in expression level specifically in the patients who actually have developed rheumatoid arthritis from pre-rheumatoid arthritis. Thus, the biomarkers can be advantageously used for assessing the disease activity of rheumatoid arthritis, diagnosing rheumatoid arthritis, and predicting the onset of rheumatoid arthritis.

Description

Biomarkers for evaluating disease activity, diagnosis and prediction of disease activity in rheumatoid arthritis

The present invention relates to biomarkers for evaluating disease activity, diagnosing, and predicting the onset of rheumatoid arthritis.

Lipidomics is a field of study that focuses on qualitatively and quantifying the overall phenomena and changes that occur in the metabolic process of various lipidomes that occur in vivo to find out their biological and biochemical significance. It is also aimed at research to find disease-related biomarkers. In particular, with the recent rapid development of electrospray ionization-tandem mass spectrometry (ESI-MS-MS), structural analysis of lipid molecules has become possible, and the field of shotgun lipidomics can also be used for analysis of biological tissues.

On the other hand, rheumatoid arthritis (RA) is a representative autoimmune disease, caused by inflammation of a tissue called the synovium surrounding the joint, and is known to develop in about 1-2% of the total population (Alamanosa and Drosos, Autoimmun. Rev., 4:130-136 (2005)). It is known that the onset of rheumatoid arthritis is affected by genetic-environmental factors, and that genetic factors contribute about 60%. Early rheumatoid arthritis initially affects the smaller joints, such as those of the wrists, hands, ankles, and feet, and as the disease progresses, may also affect the joints of the shoulders, elbows, knees, hips, jaw, and neck; If disease activity is not controlled, irreversible joint deformity and disability occur over time. Also, unlike other arthritic conditions that only affect areas within or around the joints, rheumatoid arthritis is a systemic disease that can cause inflammation in tissues outside the joints throughout the body, including the skin, blood vessels, heart, lungs and muscles. .

In order to treat rheumatoid arthritis by slowing down or stopping the progression of the disease in terms of tissue destruction, cartilage loss and joint erosion, it is important to analyze the relationship between the progression and activity of rheumatoid arthritis due to the activation of factors related to the pathogenesis. For example, it has been reported that better remission occurs when disease activity is monitored periodically.

Conventional clinical methods for measuring the disease activity of rheumatoid arthritis include the degree of pain in 28 joints such as the shoulder, elbow, wrist, knee, and finger, the number of joints showing tenderness and swelling, the erythrocyte sedimentation rate (ESR), or C - A method of measuring inflammation levels such as C-reactive protein (CRP) was used. However, it is difficult to accurately reflect the overall activity of the disease with each of the above indicators alone, so a Disease Activity Score (DAS) was developed that comprehensively judges by combining the above indicators, and in order to measure the disease activity of rheumatoid arthritis The DAS 28 (disease activity score 28) measurement method is used. To measure DAS 28, the doctor checks for swelling and tenderness in a total of 28 joints of the patient, and the erythrocyte sedimentation rate (ESR) or C- The final disease activity score is calculated by checking the reactive protein (C reactive protein) level and summing them. Although the disease activity numbers calculated in this way are relatively accurate, it is difficult to perform several times frequently because the patient's visit and professional medical personnel must be put in. There are aspects that cannot be In addition, in order to accurately determine the patient's DAS, a skilled evaluator is required to minimize wide operator variability, which limits the use of the DAS. Therefore, it is necessary to develop a powerful biomarker capable of measuring rheumatoid arthritis activity quickly, conveniently, and immediately and frequently when necessary.

Conventional methods for diagnosing rheumatoid arthritis can be largely classified into three types. First, in most cases of rheumatoid arthritis (RA), the degree of swelling, temperature, and restriction of movement in the joint area is checked through medical history and palpation, and clinical symptoms are relied on to investigate continuous fatigue or nodules or lymph under the skin. There is a limitation in that the diagnosis is made after the destruction of the joint has already progressed considerably. Second, there are blood tests, which are largely 1) rheumatoid factor (RF) test and 2) anti-citrullinated protein antibodies (ACPA) test. As a serological marker of rheumatoid arthritis, rheumatoid factor (RF) is included in the diagnostic criteria of the American College of Rheumatology (ACR), an international diagnostic standard. In %, there is a problem with sensitivity as it shows negative throughout the course of the disease, and there are problems with low accuracy and specificity, as it appears in other rheumatic diseases, chronic inflammation, malignant tumors, and even about 10% of some healthy people. In addition, the ACPA test was developed to replace the low accuracy of the RF test and is a method for testing specific protein antibodies in the blood. is holding Thirdly, there is an imaging method, and more specifically, RA is diagnosed through ultrasound examination, MRI examination, etc. around the RA affected area. Detailed information on the progress of RA and deformation of the affected area can be obtained, but it is expensive and requires specific equipment There is the inconvenience of having to go to a specific hospital with experts.

Since there is no appropriate treatment method for rheumatoid arthritis, which steadily deteriorates and increases severe inflammation and pain when the onset begins, early diagnosis is very important. and development is required.

An object of the present invention is to provide a lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis.

Another object of the present invention is to provide a kit for evaluating or diagnosing disease activity of rheumatoid arthritis.

Another object of the present invention is to provide an evaluation kit for predicting the onset of rheumatoid arthritis.

Another object of the present invention is to provide a method for providing information for diagnosing rheumatoid arthritis or evaluating disease activity.

In addition, an object of the present invention is to provide a method for providing information for predicting the onset of rheumatoid arthritis.

In order to achieve the above object, the present invention provides a lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis.

In addition, the present invention provides a kit for evaluating or diagnosing disease activity of rheumatoid arthritis.

In addition, the present invention provides an evaluation kit for predicting the onset of rheumatoid arthritis.

In addition, the present invention provides a method for providing information for diagnosing rheumatoid arthritis or evaluating disease activity.

In addition, the present invention provides a method for providing information for predicting the onset of rheumatoid arthritis.

Lipid biomarkers selected through lipidomics analysis in the present invention have a change in expression level depending on disease activity, and it was confirmed that disease activity of patients with rheumatoid arthritis can be classified into mild and moderate-severe levels. There is an effect that can be usefully used for the purpose of evaluating the disease activity of rheumatoid arthritis. In addition, lipid biomarkers selected through lipidomics analysis can distinguish between active rheumatoid arthritis and active rheumatoid arthritis, and their expression levels change specifically in patients with rheumatoid arthritis in the future from patients with the pre-stage of rheumatoid arthritis. It has an effect that can be usefully used for diagnosis of rheumatoid arthritis and for predicting the onset of rheumatoid arthritis.

1 is a diagram showing an OPLS-DA 2D score plot of a model set by a rheumatoid arthritis patient group of moderate to high severity (DAS28 ≧3.2) and a rheumatoid arthritis patient group of low disease activity (DAS28 <3.2) or a patient group in remission.

Figure 2 is a diagram showing the verification results of biomarker candidates selected through t-test (red line) and OPLS-DA (blue line):

Figure 3 is a diagram confirming the lipid metabolism characteristics in serum according to the disease course of rheumatoid arthritis (RA):

A: Normalized intensity of lipids identified in OA, preclinical RA, active RA (pre-treatment), post-treatment RA, persistently remitted RA group samples;

B: PLS-DA 2D score plot established by OA, preclinical RA and active RA groups (Q2=-0.02);

C: PLS-DA 2D score plot from model established by OA and active RA (Q2=0.00466);

D: PLS-DA 2D score plot from model established by OA and preclinical RA (Q2=-0.0904);

Figure 4 is a diagram confirming the lipid metabolism characteristics in serum according to the disease course of rheumatoid arthritis (RA):

E: PLS-DA 2D score plot from models established by preclinical RA and active RA (Q2=-0.363);

F: Expression level of four lipid-related pathways demonstrated by heatmap obtained by lipid ontology enrichment analysis;

G: 6 lipid biomarkers that can differentiate between OA and active RA, derived from t-test (p-value <0.05, FDR ≤0.25); and

H: 12 lipid biomarkers that can differentiate between patients with preclinical RA stage and those with later onset RA, derived from OPLS-DA 2D score plot (|r| >0.5).

Figure 5 is a diagram showing cross-validation of the OPLS-DA model and ROC curve of biomarker candidates for distinguishing OA and active RA:

A: cross-validation for the OPLS-DA model;

B: ROC curve inferred by 15 biomarkers obtained from t-test and OPLS-DA model; and

C: ROC curve inferred by the 12 biomarkers obtained from the OPLS-DA model.

Hereinafter, the present invention will be described in detail as an embodiment of the present invention with reference to the accompanying drawings. However, the following embodiments are presented as examples of the present invention, and if it is determined that detailed descriptions of well-known techniques or configurations may unnecessarily obscure the gist of the present invention, the detailed descriptions may be omitted. , the present invention is not limited thereby. Various modifications and applications of the present invention are possible within the scope of the claims described below and equivalents interpreted therefrom.

In addition, the terms used in this specification (terminology) are terms used to appropriately express preferred embodiments of the present invention, which may vary according to the intention of a user or operator or customs in the field to which the present invention belongs. Therefore, definitions of these terms will have to be made based on the content throughout this specification. Throughout the specification, when a certain component is said to "include", it means that it may further include other components without excluding other components unless otherwise stated.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice for testing the present invention, the preferred materials and methods are described herein.

In the present invention, lipids are marked according to the system name and common name (IUPAC-IUBMB) according to world standards for applied chemistry, biochemistry, and molecular biology in Lipidomics.

In one aspect, the present invention provides acyl carnitine (CAR), diacylglycerol (DG), lysophosphatidylcholine (LPC), ether-linked LPC (LPC O), phosphatidylcholine , PC), ether-linked phosphatidylethanolamine (ether-linked phosphatidylethanolamine (PEP)) and sphingomyelin (sphingomyelin (SM) containing one or more lipids selected from the group consisting of, disease activity evaluation of rheumatoid arthritis (disease activity) A lipid biomarker composition for use or diagnosis is provided.

In one embodiment, the lysophosphatidylcholine is 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24:0; Phosphatidylcholine may be 42:6, ether-bridged phosphatidylethanolamine may be 38:6 (2), and sphingomyelin may be 30:1.

In one embodiment, the acyl carnitine is 18:0, the diacylglycerol is 36:2, and the lysophosphatidylcholine is 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20:3, 20:4, 20:5 or 22:6, and the ether-bridged LPC may be 16:1, 18:0 or 18:1.

In one embodiment, the disease activity may be mild or moderate-high disease activity.

In one embodiment, the lipid biomarker composition can discriminate between arthritis and active rheumatoid arthritis.

In one aspect, the present invention provides acyl carnitine (CAR), diacylglycerol (DG), lysophosphatidylcholine (LPC), ether-linked LPC (LPC O), phosphatidylcholine , PC), ether-linked phosphatidylethanolamine (ether-linked phosphatidylethanolamine (PEP) and sphingomyelin (sphingomyelin, SM) containing one or more lipids selected from the group consisting of rheumatoid arthritis disease activity evaluation or diagnosis kit to provide.

In one embodiment, the kit may further include tools and/or reagents for collecting a biological sample from a subject or patient as well as tools and/or reagents for isolating lipids from the sample.

As used herein, the term "kit for evaluating disease activity of rheumatoid arthritis" refers to a kit containing the lipid biomarker composition for evaluating disease activity of rheumatoid arthritis of the present invention. Therefore, the expression "kit" can be used interchangeably or interchangeably with "composition".

In the present invention, the "disease activity of rheumatoid arthritis" refers to the overall degree of inflammation or progression of rheumatoid arthritis in patients with rheumatoid arthritis, and can be usefully used to evaluate response or remission to treatment.

The term "biomarker" used in the present invention refers to an index that can detect changes in the body using proteins, DNA, RNA, metabolites, etc. The degree of response to the drug can be objectively measured. Biomarkers are used to diagnose various incurable diseases such as cancer, stroke, and dementia, and in the present invention, they can be used to diagnose and evaluate rheumatoid arthritis or disease activity of rheumatoid arthritis. In addition, biomarkers can be used in the new drug development process, and through this, the safety of the new drug can be secured and the cost required in the new drug development process can be reduced.

In the present invention, the term "biomarker for evaluating disease activity of rheumatoid arthritis" or "biomarker for diagnosis, biomarker for diagnosis or diagnosis marker" refers to a substance capable of diagnosing and evaluating disease activity of rheumatoid arthritis. It includes organic biomolecules such as lipids whose expression increases or decreases depending on the activity level, and is a substance that can be diagnosed separately from osteoarthritis. It includes organic biomolecules such as lipids whose expression increases or decreases. For the purpose of the present invention, the biomarkers for evaluating the disease activity of rheumatoid arthritis are LPC 18:0, LPC 18:2, LPC 18:3, LPC 20:0, LPC 20:2, LPC 20:3, LPC 20: 4, at least one selected from the group consisting of LPC 20:5, LPC 22:6, LPC 24:0, PC 42:6, PE P-38:6 (2) and SM 30:1, and also for diagnosis of rheumatoid arthritis The biomarkers are CAR 18:0, DG 36:2, LPC 16:1, LPC 18:1, LPC 18:2, LPC 18:3, LPC 20:1, LPC 20:2, LPC 20:3, LPC 20 : 4, LPC 20:5, LPC 22:6, LPC O-16: 1, LPC O-18: 0 and LPC O-18: 1, as one or more selected from the group consisting of, specific for the disease activity of rheumatoid arthritis It is a lipid whose expression level changes with Preferably, these markers are composite markers in which two or more of these markers are included.

In one aspect, the present invention provides acyl carnitine (CAR), diacylglycerol (DG), lysophosphatidylcholine (LPC), ether-linked LPC (LPC O), phosphatidylcholine , PC), ether-linked phosphatidylethanolamine (ether-linked phosphatidylethanolamine (PEP) and sphingomyelin (sphingomyelin, SM) containing one or more lipids selected from the group consisting of, a lipid biomarker composition for predicting the onset of rheumatoid arthritis to provide.

In one embodiment, the carnitine is 18:0, the diacylglycerol is 36:2, and the lysophosphatidylcholine is 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20 :3, 20:4, 20:5 or 22:6, and the ether-bridged LPC may be 16:1, 18:0 or 18:1.

In one embodiment, the onset of rheumatoid arthritis can be predicted in a subject with pre-clinical RA.

In one embodiment, the lipid biomarker composition for predicting the onset of rheumatoid arthritis can discriminate between osteoarthritis and active rheumatoid arthritis.

In one aspect, acyl carnitine (CAR), diacylglycerol (DG), lysophosphatidylcholine (LPC), ether-linked LPC (LPC O), phosphatidylcholine (PC) ), ether-linked phosphatidylethanolamine (PEP), and sphingomyelin (sphingomyelin, SM), containing one or more lipids selected from the group consisting of, to provide an evaluation kit for predicting the onset of rheumatoid arthritis.

In the present invention, the term “kit for diagnosing rheumatoid arthritis” or “kit for predicting the onset of rheumatoid arthritis” refers to a kit containing the composition for diagnosing rheumatoid arthritis or the lipid biomarker composition for predicting the onset of rheumatoid arthritis of the present invention. As used herein, the term "diagnosis" refers to determining a subject's susceptibility to a specific disease or disorder, determining whether a subject currently has a specific disease or disorder, or suffering from a specific disease or disorder Determining the prognosis of a subject (e.g., identifying a pro-rheumatoid arthritis or active rheumatoid arthritis condition, determining the staging of rheumatoid arthritis or determining the responsiveness of rheumatoid arthritis to treatment), or therametrics (e.g. , monitoring the condition of the subject to provide information on treatment efficacy).

In the present invention, the term "marker for predicting the onset of rheumatoid arthritis" is a substance that can diagnose the onset of rheumatoid arthritis by distinguishing it from normal tissue or osteoarthritis, and its expression is increased in the group with rheumatoid arthritis compared to the normal control group or osteoarthritis group or organic biomolecules such as lipids that show a decreasing aspect. For the purpose of the present invention, the markers for predicting the onset of rheumatoid arthritis are CAR 18:0, LPC 16:1, LPC 18:1, LPC 18:2, LPC 18:3, LPC 20:2, LPC 20:3, LPC 20:4, LPC 20:5, LPC 22:6, LPC O-18:0 and LPC O-18:1, which are lipids whose expression levels specifically change in rheumatoid arthritis. Preferably, these markers are composite markers in which two or more of these markers are included.

In one aspect, the present invention is selected from the group consisting of (a) acyl carnitine, diacylglycerol, lysophosphatidylcholine, ether-bridged LPC, phosphatidylcholine, ether-bridged phosphatidylethanolamine and sphingomyelin in a biological sample isolated from a test subject Measuring the expression level of one or more lipids to be; and

(b) determining that the subject is a patient with rheumatoid arthritis when the expression level of the lipid in the sample of the subject is down-regulated compared to the expression level in the sample of the control subject; Alternatively, it provides a method for providing information for diagnosing or evaluating disease activity of rheumatoid arthritis, including determining the disease activity of rheumatoid arthritis.

In one embodiment, lysophosphatidylcholine 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24: If the concentrations of 0, phosphatidylcholine 42:6, ether-bridged phosphatidylethanolamine 38:6 (2), and sphingomyelin 30:1 were up-regulated compared to the expression level in the sample of the positive control subject, the disease activity of the subject's rheumatoid arthritis can be judged to be hardness.

In one embodiment, the mild disease activity may be DAS28<3.2.

In the present invention, the term "disease activity score with 28-joint assessment (DAS28)" is a rheumatoid arthritis activity evaluation using 28 joints, and is an evaluation method that is currently widely used. The new criteria based on DAS28, which reflects disease activity, are in line with the treatment guidelines of developed countries in Europe and America, and are expected to contribute to the appropriate drug use of patients with rheumatoid arthritis who require the administration of anti-TNF drugs. The joints included in the DAS28 are the PIP joint (10), the MCP joint (10), the arm joint (2), the elbow joint (2), the shoulder joint (2), and the knee joint (2).

In one embodiment, the positive control subject may be a patient with active rheumatoid arthritis.

In one embodiment, the control subject may be a normal subject.

In one embodiment, the biological sample may be blood or serum.

In one embodiment, the control subject may be a normal subject without developing rheumatoid arthritis, a subject without clinically developing rheumatoid arthritis, a positive RF or ACPA, but not clinically developing rheumatoid arthritis. may be an object.

In one embodiment, the lysophosphatidylcholine 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24:0, phosphatidylcholine 42 :6, ether-bridged phosphatidylethanolamine 38:6 (2), and sphingomyelin 30:1 concentrations compared to the expression level in the sample of the control subject down-regulated, the disease activity of the subject's rheumatoid arthritis is moderate to high can be judged to be

In one embodiment, in a sample of the subject, acyl carnitine 18:0, diacylglycerol 36:2, lysophosphatidylcholine 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20 :3, 20:4, 20:5 or 22:6, and the concentration of ether-bridged LPC 16:1, 18:0 or 18:1 is down-regulated relative to the expression level in a sample from a control subject, the subject has active rheumatism It can be judged that he is an arthritis patient.

In one embodiment, osteoarthritis and active rheumatoid arthritis can be differentiated by providing information for diagnosing rheumatoid arthritis or evaluating disease activity.

In one embodiment, the medium-high disease activity may be DAS28≥3.2.

In one embodiment, the expression level (concentration) of a lipid biomarker in a sample can be measured using any method known to those skilled in the art. Methods for measuring lipids include, but are not limited to, mass spectrometry, spectroscopic methods including soft ionization techniques for mass spectrometry, such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI). It doesn't work.

(b) determining that the subject is a patient with rheumatoid arthritis when the expression level of the lipid in the sample of the subject is down-regulated compared to the expression level in the sample of the control subject; Or, it provides a method for providing information for predicting the onset of rheumatoid arthritis, including the step of determining the disease activity of rheumatoid arthritis.

As used herein, the term "detection" or "measurement" means to quantify the concentration of a detected or measured object, and to measure "or"measurement" is a qualitative or qualitative concentration level of a given substance in a sample. It means assessing the presence, absence, quantity or amount (which may be an effective amount) of such a substance, including the derivation of, or otherwise assessing the value or categorization of a subject's clinical parameter.

In the present invention, determining the level of a biomarker can be performed on an untreated or unfractionated sample, and the level of a lipid biomarker in an untreated or unfractionated sample obtained from a subject can be determined.

In the present invention, determining the level of a biomarker may be quantitative or semi-quantitative. In some embodiments, quantitative determination may include determining the absolute amount or concentration (expression level) of one or more lipid metabolites. Quantitative determination can also include determining the relative amount or concentration of one or more lipid metabolites relative to one or more other metabolites.

As used herein, the term "subject" or "patient" refers to any single individual in need of treatment, including humans, apes, monkeys, cows, dogs, guinea pigs, rabbits, chickens, insects, and the like. Also included are any subjects participating in clinical research trials who do not show any clinical signs of disease or subjects participating in epidemiological studies or subjects used as controls. In addition, in the present invention, it refers to a mammal, preferably a human.

As used herein, the term "biological sample (sample)" means a biological sample obtained from a subject or patient, and encompasses various types of samples obtained from organisms that can be used for diagnosis or monitoring assays. The term encompasses blood and other liquid samples of biological origin, solid tissue samples such as biopsy specimens, or tissue cultures or cells derived therefrom and from their progeny. The term specifically covers clinical samples, and further includes cells in cell culture, cell supernatants, cell lysates, serum, plasma, urine, amniotic fluid, biological fluids and tissue samples. The term also encompasses samples that have been manipulated in any way after procurement, such as treatment with reagents, solubilization, or enrichment for a particular component. In one embodiment of the present invention, blood or serum was used as a sample.

A biological sample may be a sample of bodily fluid or body tissue of a subject. For example, a biological sample can be a sample of blood, plasma, serum, saliva, bile, urine, feces, or cerebrospinal fluid from a subject, or a sample derived from a cell, tissue, or organ from a subject. In many embodiments, it may be desirable to use blood, plasma or serum as the biological sample. A variety of techniques are available for obtaining biological samples.

The present invention will be described in more detail through the following examples. However, the following examples are only for specifying the content of the present invention, and the present invention is not limited thereto.

실시예 1. 류마티스관절염 활성도 평가 예측용 바이오 마커Example 1. Biomarker for predicting rheumatoid arthritis activity evaluation

실시예 1-1. 류마티스관절염 활성도 평가를 위한 지질 바이오마커 발굴Example 1-1. Discovery of lipid biomarkers for rheumatoid arthritis activity evaluation

Serum was collected from 42 patients with active rheumatoid arthritis (Active RA) before and after treatment to separate lipids. Specifically, study participants were enrolled in the Center for Integrative Rheumatoid Transcriptomics and Dynamics (CIRAD) cohort (2015), and RA patients were defined as meeting the 2010 ACR/EULAR RA classification criteria (6). These patients were followed until March 2021 for definitive RA development. Serum was collected from each participant at the time of cohort enrollment, and serum was collected and stored every 6 months. Disease activity of rheumatoid arthritis was evaluated through DAS28, and patients with moderate to high severity of rheumatoid arthritis with DAS28 exceeding 3.2 were divided into patients with low activity of 3.2 or less. To confirm the difference in lipid profile according to disease stage, lipid body analysis was also performed in the serum of 19 RA patients in continuous remission, defined as patients with DAS28 ≤ 2.6 at three consecutive measurements for 12 months. Lipids were extracted from serum collected from each group of patients using MTBE (tert-methyl butyl ether) method, and mass spectrometry-based lipidomics analysis was performed. Specifically, lipids were separated on a UPLC system equipped with a C18 column (2.1 × 100 mm, 1.7 μm) coupled with a guard column (2.1 × 5 mm, 1.7 μm), and data were collected. For data preprocessing, the collected data was imported into MS-DIAL ver 4.38, and batch effects were removed with LOESS (locally estimated scatterplot smoothing) algorithm. After peak annotation, unreliable lipids were removed when the relative standard deviation (%) of the quality control (QC) sample was >30%. Referenced lipids were putatively identified based on precursor ion m/z values and product ion patterns consistent with data from MS-DIAL's LipidBlast database. In order to increase the reliability of lipid identification as MSI (metabolomics standards initiative) 1, the identified lipid list was confirmed as an in-house lipid library including retention time. All lipid features were normalized according to the median intensity of each sample, and a t-test was performed to obtain FDR, and a paired t-test was used to compare groups before and after treatment. To compare three or more groups, significant differences were identified using Tukey's HSD and one-way ANOVA. Suspected outliers were excluded based on comprehensive analysis using heatmap, PCA, and random forest algorithms, correlation analysis was performed using the Hmisc package of R, and Pearson's correlation was determined using the rcorr function. In addition, two different multivariate statistical analysis models (unsupervised and supervised) were applied to distinguish groups (unsupervised, PCA; supervised, PLS-DA). The PLS-DA model was cross-validated using leave-one-out cross validation (LOOCV), and the Q2 value was used to estimate the overfitting value of the model. In addition, in order to further confirm the detailed differences between the patient groups, OPLS-DA (orthogonal projections to latent structures-discriminant analysis) analysis was performed. In addition, biomarker candidates are | r | Selection was made when <0.5 and when the FDR value was <0.25 in the t-test. In addition, for lipid ontology analysis, the intensity of identified lipids was normalized and input into the analysis, and changes in lipid metabolism according to RA stage were analyzed. Multivariate exploratory (ROC) analysis was performed with MCCV (Monte-Carlo cross validation), and the AUG of the ROC curve was calculated with the Random Forests algorithm.

[Table 1]

(LPC: lysophosphatidylcholine; PE P: ether-linked phosphatidylethanoloamine; and SM: sphingomyelin)

As a result, it was found that there was a difference in lipid body expression depending on the disease activity (Fig. 1), and 11 biomarkers (LPC 18:0, LPC 18:2, LPC 18:3, LPC 20: 0, LPC 20:2, LPC 20:3, LPC 20:4, LPC 20:5, LPC 22:6, LPC 24:0 and PE P-38:6 (2)) and 7 biopsies by t-test Markers (LPC 18:2, LPC 18:3, LPC 20:3, LPC 22:6, LPC 24:0, PC 42:6 and SM 30:1) were identified (Table 1).

실시예 1-2. 류마티스관절염 활성도 평가를 위한 지질 바이오마커의 검증Example 1-2. Validation of lipid biomarkers for evaluating rheumatoid arthritis activity

As a result of evaluating the function of the lipid biomarkers in 61 samples collected at different times and measured in different batches as a validation cohort, 11 biomarkers discovered by OPLS-DA were AUC = 0.669 and 7 discovered by t-test The dog biomarker was found to be able to distinguish rheumatoid arthritis patients with medium-high activity and patients with low activity with AUC = 0.650, which was confirmed to be at a level similar to CRP, which is most commonly used as a single biomarker in the past. became (Fig. 2). Accordingly, it can be seen that the lipids in Table 2 below can be used as lipid markers specific for the activity of rheumatoid arthritis.

[Table 2]

실시예 2. 류마티스 관절염의 진단 및 발병 예측용 바이오마커Example 2. Biomarkers for diagnosing and predicting the onset of rheumatoid arthritis

실시예 2-1.Example 2-1. 류마티스 관절염 환자 혈청 수집Serum collection from patients with rheumatoid arthritis

Patients with rheumatoid arthritis (Active RA), osteoarthritis (OA), and patients with pre-clinical rheumatoid arthritis (Pre-clinical RA) in Table 3 (CCP antibody and rheumatoid factor titers in blood tests were 3 times the upper limit of normal) above) serum was collected to separate lipids. Specifically, study participants were enrolled in the Center for Integrative Rheumatoid Transcriptomics and Dynamics (CIRAD) cohort, which consisted of RA patients, preclinical RA and OA patients (2015), and RA patients met the 2010 ACR/EULAR RA classification criteria (6). Preclinical RA patients were defined as those with arthralgia who did not yet meet the 2010 ACR/EULAR classification criteria but demonstrated an increase in RF and/or ACPA >3-fold above the normal limit. These patients were followed until March 2021 for definitive RA development. Serum was collected from each participant at the time of cohort enrollment, and in the case of RA patients, serum was collected and stored every 6 months. To identify changes in the lipid body according to treatment results, 42 patients with moderate disease activity (DAS28 >3.2) at baseline were selected, and their serum samples were analyzed at baseline and DMARDs (disease modifying anti-rheumatic drugs). They were collected after 6 months of treatment. In addition, to confirm the difference in the lipid body profile according to the disease stage, 19 RA patients in continuous remission, defined as patients with DAS28 ≤ 2.6 at 3 consecutive measurements for 12 months, were selected. In addition, 18 pre-clinical RA patients not receiving DMARDs or lipid-lowering drugs were selected. Age- and sex-matched OA patients were selected as controls.

[Table 3]

(ACPA: anti-citrullinated peptide antibody; BMI: body mass index; CRP: c-reactive protein; ESR: erythrocyte sedimentation rate; DAS28: disease activity score in 28 joints; HCQ: hydroxychloroquine; MTX: methotrexate; LEF: leflunomide; RF : rheumatoid factor; SR: sustained remission; and SSZ: sulfasalazine)

실시예 2-2.Example 2-2. 류마티스 관절염 단계별 특이적 지질 바이오마커 선발Selection of specific lipid biomarkers for each stage of rheumatoid arthritis

Example 2-2-1. Separation of lipids from serum and analysis of lipid bodies

Lipids were extracted from serum collected from each group of patients in Example 2-1 using MTBE (tert-methyl butyl ether) method, and mass spectrometry-based lipidomics analysis was performed. . Specifically, lipids were separated on a UPLC system equipped with a C18 column (2.1 × 100 mm, 1.7 μm) coupled with a guard column (2.1 × 5 mm, 1.7 μm), and data were collected. For data preprocessing, the collected data was imported into MS-DIAL ver 4.38, and batch effects were removed with LOESS (locally estimated scatterplot smoothing) algorithm. After peak annotation, unreliable lipids were removed when the relative standard deviation (%) of the quality control (QC) sample was >30%. Referenced lipids were putatively identified based on precursor ion m/z values and product ion patterns consistent with data from MS-DIAL's LipidBlast database. In order to increase the reliability of lipid identification as MSI (metabolomics standards initiative) 1, the identified lipid list was confirmed as an in-house lipid library including retention time.

As a result, after data preprocessing and lipid identification, 238 individual lipids corresponding to 12 lipid subclasses were obtained. Prior to statistical analysis, normalized intensities of all identified lipids in serum samples from pre-clinical RA and active RA (before treatment) showed a similar pattern, but in samples obtained from RA patients in sustained remission. The normalized intensities of all identified lipids showed a similar pattern to the OA control (Fig. 3A). In addition, it was found that there was a difference in the expression of lipid bodies according to the disease stage of rheumatoid arthritis (Fig. 3A), and the distribution of lipid bodies in patients with the pre-stage of rheumatoid arthritis (pre-clinical) between osteoarthritis patients and active rheumatoid arthritis patients It was confirmed that (Fig. 3B).

Example 2-2-2. Rheumatoid arthritis specific lipid biomarker selection and validation

All lipid features were normalized according to the median intensity of each sample, and a t-test was performed to obtain FDR, and a paired t-test was used to compare groups before and after treatment. To compare three or more groups, significant differences were identified using Tukey's HSD and one-way ANOVA. Suspected outliers were excluded based on comprehensive analysis using heatmap, PCA, and random forest algorithms, correlation analysis was performed using R's Hmisc package, and Pearson's correlation was determined using the rcorr function. In addition, two different multivariate statistical analysis models (unsupervised and supervised) were applied to distinguish groups (unsupervised, PCA; supervised, PLS-DA). The PLS-DA model was cross-validated using leave-one-out cross validation (LOOCV), and the Q2 value was used to estimate the overfitting value of the model. In addition, in order to further confirm the detailed differences between the patient groups, OPLS-DA (orthogonal projections to latent structures-discriminant analysis) analysis was performed. In addition, biomarker candidates are | r | Selection was made when <0.5 and when the FDR value was <0.25 in the t-test. In addition, for lipid ontology analysis, the intensity of identified lipids was normalized and input into the analysis, and changes in lipid metabolism according to RA stage were analyzed. Multivariate exploratory ROC analysis was performed with MCCV (Monte-Carlo cross validation), and the AUG of the ROC curve was calculated with the Random Forests algorithm.

[Table 4]

(CAR: acyl carnitine; DG: diacylglycerol; LPC: lysophosphatidylcholine; and LPC O-: ether-linked LPC)

As a result, it was found that there were lipid bodies distinguished in osteoarthritis, pre-clinical phase to active phase, and active phase (Fig. 3C to 3E), and as a biomarker candidate, T Six lipid biomarkers (CAR18:0, DG 36:2, LPC 16:1, LPC 18:1, LPC 20:1, LPC O-16:1) determined by -test (bold in Table 4), OPLS -12 lipid biomarkers (CAR 18:0, LPC 16:1, LPC 18:1, LPC 18:2, LPC 18:3, LPC 20:2, LPC 20:3, LPC 20: 4, LPC 20:5, LPC 22:6, LPC O-18:0, LPC O-18:1) were excavated (Table 4). As a result of verifying them through biomarker analysis, it was found that OA and active RA were distinguished (AUC = 0.656), and in particular, six lipid biomarkers confirmed that osteoarthritis and rheumatoid arthritis (Active RA) could be distinguished. (AUC = 0.718) (Fig. 4G). In addition, as a result of lipid ontology analysis, it was confirmed that four lipid pathways, monoalkylglycerophosphocholines, monoacylglycerophosphocholines, lysoglycerophospholipids, and C12:0, whose metabolism was significantly changed depending on the RA stage, were all downregulated in active RA patients compared to OA or preclinical RA patients. It was found to be (Fig. 4F). The AUC of a total of 15 biomarker combinations identified above was 0.656, and the AUC of 12 biomarkers identified in the OPLS-DA plot was 0.583 (FIG. 5), and this combination was found in pre-clinical patients with rheumatoid arthritis. Patients (7 patients) who actually developed rheumatoid arthritis later in (17 patients) were well distinguished by AUC = 0.708 (FIG. 4H). From this, it can be seen that the lipids act as serum biomarkers that can predict whether preclinical RA will progress to established RA (Table 5).

[Table 5]

Claims

Acyl carnitine (CAR), diacylglycerol (DG), lysophosphatidylcholine (LPC), ether-linked LPC (LPC O), phosphatidylcholine (PC), ether-linked A lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis, comprising at least one lipid selected from the group consisting of ether-linked phosphatidylethanolamine (PEP) and sphingomyelin (SM) .
The method of claim 1, wherein the lysophosphatidylcholine is 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24:0; Phosphatidylcholine is 42:6, ether-bridged phosphatidylethanolamine is 38:6 (2), and sphingomyelin is 30:1, a lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis.
The method of claim 1, wherein the acyl carnitine is 18:0, the diacylglycerol is 36:2, and the lysophosphatidylcholine is 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20:3, 20:4, 20:5 or 22:6, and ether-bridged LPC is 16:1, 18:0 or 18:1, a lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis.
The lipid biomarker composition for evaluating or diagnosing the disease activity of rheumatoid arthritis according to claim 2, wherein the disease activity is mild or moderate-high disease activity.
The lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis according to claim 3, which predicts the onset of rheumatoid arthritis in a pre-clinical RA subject.
The lipid biomarker composition for evaluating or diagnosing disease activity of rheumatoid arthritis according to claim 3, which differentiates between osteoarthritis and active rheumatoid arthritis.
A kit for evaluating or diagnosing disease activity of rheumatoid arthritis, comprising the composition of claim 1.
Acyl carnitine (CAR), diacylglycerol (DG), lysophosphatidylcholine (LPC), ether-linked LPC (LPC O), phosphatidylcholine (PC), ether-linked A lipid biomarker composition for predicting the onset of rheumatoid arthritis, comprising at least one lipid selected from the group consisting of ether-linked phosphatidylethanolamine (PEP) and sphingomyelin (SM).
9. The method of claim 8, wherein the lysophosphatidylcholine is 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24:0; A lipid biomarker composition for predicting the onset of rheumatoid arthritis, wherein phosphatidylcholine is 42:6, ether-bridged phosphatidylethanolamine is 38:6 (2), and sphingomyelin is 30:1.
9. The method of claim 8, wherein the acyl carnitine is 18:0, the diacylglycerol is 36:2, and the lysophosphatidylcholine is 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20:3, 20:4, 20:5 or 22:6, and the ether-bridged LPC is 16:1, 18:0 or 18:1, a lipid biomarker composition for predicting the onset of rheumatoid arthritis.
The lipid biomarker composition for predicting the onset of rheumatoid arthritis according to claim 8, which predicts the onset of rheumatoid arthritis in a pre-clinical RA subject.
The lipid biomarker composition for predicting the onset of rheumatoid arthritis according to claim 9, wherein the disease activity is mild or moderate-high disease activity.
The lipid biomarker composition for predicting the onset of rheumatoid arthritis according to claim 10, which differentiates between osteoarthritis and active rheumatoid arthritis.
An evaluation kit for predicting the onset of rheumatoid arthritis, comprising the composition of claim 8.
(a) the expression level of one or more lipids selected from the group consisting of acyl carnitine, diacylglycerol, lysophosphatidylcholine, ether-bridged LPC, phosphatidylcholine, ether-bridged phosphatidylethanolamine and sphingomyelin in a biological sample isolated from a test subject; measuring; and

(b) determining that the subject is a patient with rheumatoid arthritis when the expression level of the lipid in the sample of the subject is down-regulated compared to the expression level in the sample of the control subject; Or a method for providing information for diagnosing or evaluating disease activity of rheumatoid arthritis, comprising determining the disease activity of rheumatoid arthritis.
[Claim 16] The method according to claim 15, wherein the biological sample is blood or serum.
16. The method of claim 15, wherein the lysophosphatidylcholine is 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24:0; A method for providing information for diagnosis or disease activity evaluation of rheumatoid arthritis, wherein phosphatidylcholine is 42:6, ether-bridged phosphatidylethanolamine is 38:6 (2), and sphingomyelin is 30:1.
16. The method of claim 15, wherein the lysophosphatidylcholine 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24: If the concentrations of 0, phosphatidylcholine 42:6, ether-bridged phosphatidylethanolamine 38:6 (2), and sphingomyelin 30:1 were up-regulated compared to the expression level in the sample of the positive control subject, the disease activity of the subject's rheumatoid arthritis A method for providing information for diagnosis or disease activity evaluation of rheumatoid arthritis, which is determined to be mild.
[Claim 19] The method according to claim 17 or 18, wherein the mild disease activity is DAS28<3.2.
19. The lysophosphatidylcholine according to claim 17 or 18, 18:0, 18:2, 18:3, 20:0, 20:2, 20:3, 20:4, 20:5, 22:6 or 24: When the concentrations of 0, phosphatidylcholine 42:6, ether-bridged phosphatidylethanolamine 38:6 (2), and sphingomyelin 30:1 were downregulated compared to the expression level in the sample of the control subject, the disease activity of the subject's rheumatoid arthritis A method for providing information for diagnosis or disease activity evaluation of rheumatoid arthritis, which is judged to be of moderate to high severity.
16. The method of claim 15, wherein the acyl carnitine is 18:0, the diacylglycerol is 36:2, and the lysophosphatidylcholine is 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20:3, 20:4, 20:5 or 22:6, and the ether-bridged LPC is 16:1, 18:0 or 18:1, providing information for diagnosis or disease activity evaluation of rheumatoid arthritis Way.
16. The method of claim 15, wherein in the sample of the subject, acyl carnitine 18:0, diacylglycerol 36:2, lysophosphatidylcholine 16:1, 18:1, 18:2, 18:3, 20:1, 20:2, 20 :3, 20:4, 20:5 or 22:6, and the concentration of ether-bridged LPC 16:1, 18:0 or 18:1 is down-regulated relative to the expression level in a sample from a control subject, the subject has active rheumatism A method for providing information for diagnosis or disease activity evaluation of rheumatoid arthritis, which is determined to be an arthritis patient.
The method according to claim 21 or 22, which predicts the onset of rheumatoid arthritis in a pre-clinical RA subject, provides information for diagnosing rheumatoid arthritis or evaluating disease activity.
The method according to claim 21 or 22, wherein osteoarthritis and active rheumatoid arthritis are differentiated, and information is provided for diagnosing rheumatoid arthritis or evaluating disease activity.
23. The method of claim 21 or 22, wherein the medium-high disease activity is DAS28≧3.2.
(a) the expression level of one or more lipids selected from the group consisting of acyl carnitine, diacylglycerol, lysophosphatidylcholine, ether-bridged LPC, phosphatidylcholine, ether-bridged phosphatidylethanolamine and sphingomyelin in a biological sample isolated from a test subject; measuring; and

(b) determining that the subject is a patient with rheumatoid arthritis when the expression level of the lipid in the sample of the subject is down-regulated compared to the expression level in the sample of the control subject; Or a method for providing information for predicting the onset of rheumatoid arthritis, comprising determining the disease activity of rheumatoid arthritis.