Microrna biomarker for metabolic disease prediction or diagnosis, and use thereof

Abstract

The present specification relates to a microRNA biomarker for metabolic disease prediction or diagnosis, and more specifically, by measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p, metabolic disease may be predicted or diagnosed with high accuracy, or a prognosis of metabolic disease may be predicted.

Description

MicroRNA biomarkers for predicting or diagnosing metabolic diseases and their uses

[0001] Cross-reference to related applications

[0002] This application claims priority to Republic of Korea Patent Application No. 10-2023-0190297, filed December 22, 2023, the entire contents of which are incorporated herein by reference. This application also claims priority to Republic of Korea Patent Application No. 10-2024-0095592, filed July 19, 2024, the entire contents of which are incorporated herein by reference.

[0003] The present specification relates to a composition for predicting or diagnosing a metabolic disease, a kit for predicting or diagnosing a metabolic disease, and a method for providing information for predicting or diagnosing a metabolic disease.

[0004]

[0005] [National Research and Development Project Supporting This Invention]

[0006] [Project ID]1465039479

[0007] [Assignment Number] HI22C0444000023

[0008] [Ministry Name] Ministry of Health and Welfare

[0009] [Name of Project Management (Specialist) Institution] Korea Health Industry Development Institute

[0010] [Research Project Name] K-Medi Convergence Talent Development Support Project

[0011] [Research Project Name] To elucidate the mechanism of improvement in insulin sensitivity of adipose tissue by breastfeeding

[0012] Multi-ome analysis and discovery of diabetes treatment targets

[0013] [Name of Project Performing Organization] Bundang Seoul National University Hospital

[0014] Research Period: April 1, 2022 - December 31, 2023

[0015]

[0016] [National Research and Development Project Supporting This Invention]

[0017] [Project ID]1465037728

[0018] [Project Number] 2022-ER1107-00

[0019] [Ministry Name] Ministry of Health and Welfare

[0020] [Name of task management (specialized) organization] Korea Disease Control and Prevention Agency

[0021] [Research Project Name] Chronic Disease Management Technology Development Research

[0022] [Research Project Name] MicroRNA expression before and after metabolic surgery

[0023] Current Change Research

[0024] [Name of Project Performing Organization] Bundang Seoul National University Hospital

[0025] Research Period: August 23, 2022 - December 20, 2023

[0026]

[0027] [National Research and Development Project Supporting This Invention]

[0028] [Project ID]1711196365

[0029] [Assignment Number] 00218616

[0030] [Ministry Name] Ministry of Science and ICT

[0031] [Name of Project Management (Specialist) Institution] National Research Foundation of Korea

[0032] [Research Project Name] Biomedical Technology Development

[0033] [Research Project Name] Treatment of Metabolic Diseases by Controlling Senofibrosis in White Adipose Tissue

[0034] Development of core technologies

[0035] [Name of Project Performing Organization] Ulsan National Institute of Science and Technology

[0036] Research Period: April 1, 2023 - December 31, 2027

[0037]

[0038] [National Research and Development Project Supporting This Invention]

[0039] [Project ID]1711195966

[0040] [Assignment Number] 00222910

[0041] [Ministry Name] Ministry of Science and ICT

[0042] [Name of Project Management (Specialist) Institution] National Research Foundation of Korea

[0043] [Research Project Name] Biomedical Technology Development

[0044] [Research Project Name] 5 Major Quality Problems through a Customized Future Medical Research Center in the Era of 6P Medicine

[0045] Development of medical field application technology and training of physician scientists

[0046] [Name of Project Performing Organization] Bundang Seoul National University Hospital

[0047] [Research Period] April 1, 2023 - December 31, 2026

[0048]

[0049] [National Research and Development Project Supporting This Invention]

[0050] [Project ID]1711189406

[0051] [Assignment Number] 2018R1A5A2024425

[0052] [Ministry Name] Ministry of Science and ICT

[0053] [Name of Project Management (Specialist) Institution] National Research Foundation of Korea

[0054] [Research Project Name] Group Research Support

[0055] [Research Project Name] Biolipid Interactomics Research Center

[0056] [Name of Project Performing Organization] Seoul National University

[0057] [Research Period] March 1, 2023 - February 29, 2024

[0058]

[0059] [National Research and Development Project Supporting This Invention]

[0060] [Project Number] RS-2024-00403679

[0061] [Ministry Name] Ministry of Health and Welfare

[0062] [Name of the task management (specialized) organization] Korea Health Industry Development Institute

[0063] [Research Project Name] Global Physician Scientist Training Program

[0064] [Research Project Name] Identifying Treatment Targets for Maternal-Fetal Metabolic Disorders through Multi-Omegame Analysis of Pregnancy Adipose Tissue

[0065] [Name of Project Performing Organization] Bundang Seoul National University Hospital

[0066] [Research Period] April 1, 2024 - December 31, 2024

[0067]

[0068] In recent years, modern people's shift toward a high-calorie diet, lack of exercise, and increased stress have disrupted the body's homeostasis, leading to a sharp increase in various metabolic diseases. Metabolic diseases encompass serious conditions that threaten human health and life, such as obesity, hypertension, diabetes, and hyperlipidemia.

[0069] Obesity is a condition characterized by excessive fat tissue in the body. It can be caused by a variety of factors, including psychological and social factors, genetics, disease, and medications. The prevalence of obesity, particularly due to excessive calorie intake or a high-fat diet, is on the rise in modern times. The severity of obesity is increasingly recognized because it can lead to various complications, including metabolic syndrome (high blood pressure, hyperlipidemia, diabetes), fatty liver disease, joint problems, and cancer. Furthermore, obesity can lead to not only physical ailments but also mental health problems, such as social isolation and alienation, lack of confidence, and depression. Therefore, the need for obesity prevention and treatment is recognized as crucial.

[0070] Meanwhile, obesity can be measured using height and weight, and diagnosed by measuring skinfold thickness using calipers. Modified Broca's method and body mass index (BMI) are used to quantify the degree of obesity. However, these diagnostic methods rely solely on limited physical characteristics, making it difficult to accurately diagnose obesity using these measures alone. Therefore, there is a need to identify obesity-specific biomarkers for more accurate and earlier diagnosis.

[0071]

[0072] One aspect of the present disclosure is to provide a composition for predicting or diagnosing metabolic diseases.

[0073] One aspect of the present disclosure is to provide a kit for predicting or diagnosing metabolic diseases.

[0074] One aspect of this specification is to provide a method for providing information for predicting or diagnosing metabolic diseases.

[0075]

[0076] A composition for predicting or diagnosing a metabolic disease according to one aspect of the present disclosure comprises a preparation for measuring the expression level of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p.

[0077] A kit for predicting or diagnosing a metabolic disease according to one aspect of the present specification comprises the composition for predicting or diagnosing a metabolic disease.

[0078] A method for providing information for predicting or diagnosing a metabolic disease according to one aspect of the present specification comprises the steps of measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample isolated from a subject; and

[0079] A step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease, when the expression level of the above miR 122-5p is higher than that of the normal control group and the expression levels of miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p and miR 106a-5p are lower than that of the normal control group; is included.

[0080] A method for providing information for predicting or diagnosing a metabolic disease according to one aspect of the present specification comprises the steps of measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample isolated from a subject; and

[0081] A step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease, when the LDA score calculated by [Mathematical Formula 1] below is 1.5 or higher; includes:

[0082] [Mathematical Formula 1]

[0083] [LDA score] = 1.867 * [hsa-miR-122-5p] + 1.186 * [hsa-miR-93-5p] -1.406 * [hsa-miR-17-5p] + 1.188 * [hsa-miR-20a-5p] -1.275 * [hsa-miR-20b-5p] -0.924 * [hsa-miR-106a-5p]

[0084] (In the above mathematical formula 1,

[0085] [hsa-miR-122-5p] represents the expression level of the above miR 122-5p,

[0086] [hsa-miR-93-5p] represents the expression level of the above miR 93-5p,

[0087] [hsa-miR-17-5p] represents the expression level of the above miR 17-5p,

[0088] [hsa-miR-20a-5p] represents the expression level of the above miR 20a-5p,

[0089] [hsa-miR-20b-5p] represents the expression level of the above miR 20b-5p,

[0090] [hsa-miR-106a-5p] indicates the expression level of the above miR 106a-5p.)

[0091]

[0092] By using a composition for predicting or diagnosing a metabolic disease and / or a kit for predicting or diagnosing a metabolic disease, or a method for providing information for predicting or diagnosing a metabolic disease according to one aspect of the present specification, it is possible to predict or diagnose a metabolic disease, such as obesity, with high accuracy, and to predict the prognosis of a metabolic disease.

[0093]

[0094] Figure 1 shows the results of selecting miRNA markers with significant differences in expression levels in serum samples between a group of obese patients before metabolic surgery and a normal group after metabolic surgery.

[0095] Figure 2 shows the results of selecting miRNA markers with significant differences in expression levels in adipose tissue samples between the obese patient group and the normal group before undergoing metabolic surgery.

[0096] Figure 3 shows six miRNA markers whose expression levels differed between the obese patient group and the normal group before metabolic surgery in both serum and adipose tissue samples.

[0097] Figure 4 shows the predictive effect of the miR 122-5p marker on metabolic diseases.

[0098] Figure 5 shows the predictive effect of the miR 93-5p marker on metabolic diseases.

[0099] Figure 6 shows the predictive effect of the miR 17-5p marker on metabolic diseases.

[0100] Figure 7 shows the predictive effect of the miR 20a-5p marker on metabolic diseases.

[0101] Figure 8 shows the predictive effect of the miR 20b-5p marker on metabolic diseases.

[0102] Figure 9 shows the predictive effect of the miR 106a-5p marker on metabolic diseases.

[0103] Figure 10 shows the metabolic disease prediction effect of six miRNA markers according to one aspect of the present specification and the prediction model formula using the six miRNA markers.

[0104]

[0105] It should be understood that the various embodiments and terms used in this document are not intended to limit the technical features described in this document to specific embodiments, but rather to include various modifications, equivalents, or substitutes of the embodiments.

[0106]

[0107] Obesity (especially severe obesity, which is difficult to treat) and metabolic diseases associated with obesity can be effectively improved through metabolic surgery.

[0108] Based on this, the present inventors discovered biomarkers (microRNAs (miRNAs) miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and / or miR 106a-5p) for predicting or diagnosing obesity and / or accompanying metabolic diseases by utilizing biomarkers (blood, tissue) before and after metabolic surgery. In particular, the present inventors discovered biomarkers that can predict metabolic diseases with higher accuracy by selecting miRNAs that have significantly large differences in expression levels in common between blood samples and adipose tissue samples. In one embodiment, the present inventors analyzed miRNAs contained in the blood and adipose tissue of a normal group (or a normal group that underwent metabolic surgery) and a metabolic disease group (a group of obese patients who did not undergo metabolic surgery). Through analysis, we selected miRNAs with significantly different expression levels between the normal group and the metabolic disease group in both blood and adipose tissue, and confirmed the predictive effect of metabolic diseases using the selected miRNAs.

[0109]

[0110] One aspect of the present disclosure provides a composition for predicting or diagnosing a metabolic disease, comprising an agent for measuring the expression level of at least one of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p.

[0111] One aspect of the present disclosure provides a composition for predicting or diagnosing a metabolic disease, comprising a formulation for measuring the expression level of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p.

[0112] In an exemplary embodiment, miR 122-5p may be comprised of a base sequence represented by SEQ ID NO: 1. miR 122-5p may include a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 1. miR 122-5p may be comprised of a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 1.

[0113] In an exemplary embodiment, miR 93-5p may be comprised of a base sequence represented by SEQ ID NO: 2. miR 93-5p may include a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 2. miR 93-5p may be comprised of a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 2.

[0114] In an exemplary embodiment, miR 17-5p may be comprised of a base sequence represented by SEQ ID NO: 3. miR 17-5p may include a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 3. miR 17-5p may be comprised of a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 3.

[0115] In an exemplary embodiment, miR 20a-5p may be comprised of a base sequence represented by SEQ ID NO: 4. miR 20a-5p may include a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 4. miR 20a-5p may be comprised of a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 4.

[0116] In an exemplary embodiment, miR 20b-5p may be comprised of a base sequence represented by SEQ ID NO: 5. miR 20b-5p may include a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 5. miR 20b-5p may be comprised of a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 5.

[0117] In an exemplary embodiment, miR 106a-5p may be comprised of a base sequence represented by SEQ ID NO: 6. miR 106a-5p may include a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 6. miR 106a-5p may be comprised of a base sequence having at least 70%, at least 80%, at least 90%, or at least 95% sequence homology with the base sequence represented by SEQ ID NO: 6.

[0118] The sequences of sequence numbers 1 to 6 are listed in Table 1.

[0119] Sequence number miRNA sequence (5'->3') or gene id1 miR 122-5pUGGAGUGUGACAAUGGUGUUUG, geneID:4069062 miR 93-5pCAAAGUGCUGUUCGUGCAGGUAG, geneID:4070503 miR 17-5pCAAAGUGCUUACAGUGCAGGUAG, geneID:4069524 miR 20a-5pUAAAGUGCUUAUAGUGCAGGUAG, geneID:4069825 miR 20b-5pCAAAGUGCUCAUAGUGCAGGUAG, geneID:5740326 miR 106a-5pAAAAGUGCUUACAGUGCAGGUAG, geneID:406899

[0120] Each of the above miRNAs may be miRNAs contained in a sample (e.g., blood and / or adipose tissue) of a subject suspected of having a metabolic disease.

[0121]

[0122] In one exemplary embodiment, the metabolic disease may be at least one selected from the group consisting of, but not limited to, obesity, hypertension, diabetes, insulin resistance syndrome, hyperlipidemia, and cardiovascular disease. In one exemplary embodiment, the metabolic disease may be a metabolic disease accompanied by obesity.

[0123] In this specification, “metabolic disease prediction” may refer to predicting or diagnosing whether a subject is likely to develop a metabolic disease, whether the subject is relatively likely to develop a metabolic disease, what the causative factor of a metabolic disease is, or whether a metabolic disease has already developed. In addition, “metabolic disease prediction” may refer to predicting or diagnosing the prognosis of a metabolic disease. According to an exemplary embodiment, the prediction of a metabolic disease may be a prediction of the prognosis of a metabolic disease. The composition, kit, and / or information providing method according to one aspect of the present specification may be used for predicting, diagnosing, and / or predicting the prognosis of a metabolic disease.

[0124] In this specification, "diagnosis" may refer to a comprehensive assessment of a patient's condition. The assessment may include the name of the disease, its etiology, type, severity, detailed description of the condition, and the presence or absence of complications. In this specification, diagnosis may also refer to determining the presence or absence of a metabolic disease and / or its progression level.

[0125] In an exemplary embodiment, the expression level may be an expression amount. In an exemplary embodiment, the expression level may be measured according to a method commonly used in the art. Examples include, but are not limited to, reverse transcriptase polymerase chain reaction (RT-PCR), competitive RT-PCR, real-time RT-PCR, RNase protection assay (RPA), Northern blotting, or gene chips.

[0126] In one exemplary embodiment, the agent for measuring expression levels may be a substance that specifically binds to miRNA, including, but not limited to, a primer or a probe. In one exemplary embodiment, the agent may be a sense and antisense primer; or a probe.

[0127] A primer is a short genetic sequence that serves as the starting point for DNA synthesis. It can refer to an oligonucleotide synthesized for purposes such as diagnosis and DNA sequencing. Primers are typically synthesized to a length of 15 to 30 base pairs, but this can vary depending on the intended use. They can also be modified through known methods such as methylation and capping.

[0128] A probe can refer to a nucleic acid capable of specifically binding to miRNA, typically a few to several hundred bases in length, produced through enzymatic, chemical, or synthetic processes. The presence or absence of miRNA can be confirmed by labeling the probe with a radioisotope, enzyme, or fluorescent substance. The probe can be designed and modified using known methods.

[0129] Primers or probes can be appropriately designed by those skilled in the art based on known sequences. For example, primers or probes can be chemically synthesized using phosphoramidite solid support methods or other well-known methods. These nucleic acid sequences can also be modified using various methods known in the art.

[0130] In an exemplary embodiment, a composition for predicting or diagnosing a metabolic disease according to one aspect of the present disclosure may be used to predict or diagnose a metabolic disease by measuring the level of miRNA expression from at least one selected from the group consisting of blood, serum, plasma, tissue, saliva, and urine isolated from a subject.

[0131] In an exemplary embodiment, the metabolic disease prediction or diagnosis may be performed by predicting that the subject has a metabolic disease or is likely to develop a metabolic disease when the LDA score value calculated by applying the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p to the following mathematical formula 1 is 1.5 or higher:

[0132] [Mathematical Formula 1]

[0133] [LDA score] = 1.867 * [hsa-miR-122-5p] + 1.186 * [hsa-miR-93-5p] -1.406 * [hsa-miR-17-5p] + 1.188 * [hsa-miR-20a-5p] -1.275 * [hsa-miR-20b-5p] -0.924 * [hsa-miR-106a-5p]

[0134] (In the above mathematical formula 1,

[0135] [hsa-miR-122-5p] represents the expression level of the above miR 122-5p,

[0136] [hsa-miR-93-5p] represents the expression level of the above miR 93-5p,

[0137] [hsa-miR-17-5p] represents the expression level of the above miR 17-5p,

[0138] [hsa-miR-20a-5p] represents the expression level of the above miR 20a-5p,

[0139] [hsa-miR-20b-5p] represents the expression level of the above miR 20b-5p,

[0140] [hsa-miR-106a-5p] indicates the expression level of the above miR 106a-5p.)

[0141] As for the above mathematical expression 1, it is the same as that described below, and thus specific details are omitted.

[0142]

[0143] In addition, one aspect of the present specification provides a kit for predicting or diagnosing a metabolic disease, comprising the composition described above.

[0144] Compositions for predicting or diagnosing metabolic diseases and metabolic diseases have been described above, so detailed descriptions thereof will be omitted.

[0145] One aspect of the present specification may provide a kit for predicting or diagnosing a metabolic disease, including an agent for measuring the expression level of at least one of the aforementioned miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p. One aspect of the present specification may provide a kit for predicting or diagnosing a metabolic disease, including at least one of the aforementioned agent for measuring the expression level of miR 122-5p, the agent for measuring the expression level of miR 93-5p, the agent for measuring the expression level of miR 17-5p, the agent for measuring the expression level of miR 20a-5p, the agent for measuring the expression level of miR 20b-5p, and the agent for measuring the expression level of miR 106a-5p.

[0146] In an exemplary embodiment, the kit can quantify the expression levels of the six miRNAs (miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p).

[0147] In an exemplary embodiment, the kit may further include a preparation for measuring the expression level of at least one of the aforementioned miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p, as well as components, tools, reagents, etc. commonly used in the art to be suitable for use as a metabolic disease prediction or diagnosis kit.

[0148] In an exemplary embodiment, the component, tool or reagent may be a carrier, a label capable of generating a detectable signal, a chromophore, a solubilizer, a detergent, a buffer, a stabilizer, etc. When the label is an enzyme, it may include a substrate capable of measuring enzyme activity and a reaction terminator. The carrier may be a soluble carrier or an insoluble carrier, and a soluble carrier is a physiologically acceptable buffer known in the art, for example, PBS, and an insoluble carrier may be a polymer such as polystyrene, polyethylene, polypropylene, polyester, polyacrylonitrile, a fluororesin, a cross-linked dextran, a polysaccharide, a metal-plated magnetic microparticle on latex, other paper, glass, metal, agarose, and combinations thereof.

[0149]

[0150] In addition, one aspect of the present specification provides a method for providing information for predicting or diagnosing a metabolic disease, comprising the step of measuring the expression level of at least one of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample isolated from a subject.

[0151] In an exemplary embodiment, the information providing method may further include a step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease, when i) the expression level of the miR 122-5p is higher than that of a normal control group, or ii) the expression level of at least one of the miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p is lower than that of a normal control group.

[0152] In an exemplary embodiment, the information providing method may further include a step of determining that the subject has a poor prognosis for a metabolic disease, if i) the expression level of the miR 122-5p is higher than that of a normal control group, or ii) the expression level of at least one of the miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p is lower than that of a normal control group. In this case, the subject may be an individual suffering from a metabolic disease.

[0153] In addition, one aspect of the present specification provides a method for providing information for predicting or diagnosing a metabolic disease, including the step of measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample isolated from a subject.

[0154] As metabolic diseases have been discussed above, a detailed explanation will be omitted.

[0155] In an exemplary embodiment, the subject may be an individual at risk for developing a metabolic disease or an individual who has developed a metabolic disease. The individual may include, but is not limited to, a mammal. The mammal may include, but is not limited to, a human, a mouse, a cow, a dog, a rabbit, or a cat.

[0156] In an exemplary embodiment, the information providing method may further include a step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease, when the expression level of miR 122-5p is higher than that of a normal control group and the expression levels of miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p are lower than that of a normal control group.

[0157] In an exemplary embodiment, the information providing method may further include a step of determining that the subject has a poor prognosis for a metabolic disease when the expression level of miR 122-5p is higher than that of a normal control group and the expression levels of miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p are lower than that of a normal control group. In this case, the subject may be an individual suffering from a metabolic disease.

[0158] In an exemplary implementation example, the information providing method comprises:

[0159] The method may further include a step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease when the LDA score calculated by [Mathematical Formula 1] below is greater than or equal to a predetermined value.

[0160] In an exemplary implementation example, the information providing method comprises:

[0161] The method may further include a step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease when the LDA score calculated by [Mathematical Formula 1] below is 1.5 or higher.

[0162] In an exemplary implementation example, the information providing method comprises:

[0163] The method may further include a step of determining that the subject has a poor prognosis for a metabolic disease if the LDA score calculated by [Mathematical Formula 1] below is 1.5 or higher. In this case, the subject may be an individual suffering from a metabolic disease.

[0164] [Mathematical Formula 1]

[0165] [LDA score] = 1.867 * [hsa-miR-122-5p] + 1.186 * [hsa-miR-93-5p] -1.406 * [hsa-miR-17-5p] + 1.188 * [hsa-miR-20a-5p] -1.275 * [hsa-miR-20b-5p] -0.924 * [hsa-miR-106a-5p]

[0166] (In the above mathematical formula 1,

[0167] [hsa-miR-122-5p] represents the expression level of the above miR 122-5p,

[0168] [hsa-miR-93-5p] indicates the expression level of miR 93-5p.

[0169] [hsa-miR-17-5p] indicates the expression level of miR 17-5p,

[0170] [hsa-miR-20a-5p] indicates the expression level of miR 20a-5p.

[0171] [hsa-miR-20b-5p] indicates the expression level of miR 20b-5p.

[0172] [hsa-miR-106a-5p] indicates the expression level of miR 106a-5p.)

[0173] In this specification, * included in mathematical expression 1 means multiplication.

[0174] In this specification, miR may be hsa-miR (Homo sapiens miRNA, human microRNA, hsa-miRNA).

[0175] The above expression level is a normalized expression level value.

[0176] The above expression level may be a value converted to Transcripts Per Million (TPM).

[0177] The above mathematical expression 1 may be obtained by combining the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p using a linear or nonlinear regression analysis method.

[0178] According to one embodiment, the threshold of the above mathematical expression 1 may be 1.5.

[0179] In an exemplary embodiment, when the result obtained by applying the expression levels of six miRNAs (miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p) to the above mathematical formula 1 is 1.5 or more, it can be predicted that the subject has a metabolic disease or is likely to develop a metabolic disease, and when the result is less than 1.5, it can be predicted that the subject will not develop a metabolic disease.

[0180] In an exemplary embodiment, the sample may be at least one selected from the group consisting of blood, serum, plasma, tissue, saliva, and urine, but is not limited thereto. For example, the tissue may be adipose tissue.

[0181] The expression level of microRNAs can be measured by at least one method selected from the group consisting of polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), RNase protection assay (RPA), microarray, or northern blotting, but is not limited thereto.

[0182]

[0183] Additionally, one aspect of this specification is:

[0184] (a) a step of measuring the expression level of at least one of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample isolated from an object;

[0185] (b) a step of diagnosing the subject as having a metabolic disease or diagnosing that the subject will develop a metabolic disease when the measured expression level of miR 122-5p is higher than that of the normal control group and the expression levels of miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p are lower than that of the normal control group; and

[0186] (c) a step of administering an effective amount of a metabolic disease treatment agent to a diagnosed subject; a method for diagnosing and treating a metabolic disease is provided.

[0187] Additionally, one aspect of this specification is:

[0188] (a) a step of measuring the expression level of at least one of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample isolated from an object;

[0189] (b) a step of diagnosing the subject as having a metabolic disease or diagnosing that the subject will develop a metabolic disease when the LDA score calculated by [Mathematical Formula 1] below is greater than or equal to a predetermined value (e.g., 1.5); and

[0190] (c) a step of administering an effective amount of a metabolic disease treatment agent to a diagnosed subject; provides a method for diagnosing and treating a metabolic disease;

[0191] [Mathematical Formula 1]

[0192] [LDA score] = 1.867 * [hsa-miR-122-5p] + 1.186 * [hsa-miR-93-5p] -1.406 * [hsa-miR-17-5p] + 1.188 * [hsa-miR-20a-5p] -1.275 * [hsa-miR-20b-5p] -0.924 * [hsa-miR-106a-5p]

[0193] (In the above mathematical formula 1,

[0194] [hsa-miR-122-5p] represents the expression level of the above miR 122-5p,

[0195] [hsa-miR-93-5p] indicates the expression level of miR 93-5p.

[0196] [hsa-miR-17-5p] indicates the expression level of miR 17-5p,

[0197] [hsa-miR-20a-5p] indicates the expression level of miR 20a-5p.

[0198] [hsa-miR-20b-5p] indicates the expression level of miR 20b-5p.

[0199] [hsa-miR-106a-5p] indicates the expression level of miR 106a-5p.)

[0200] In this specification, * included in mathematical expression 1 means multiplication.

[0201] In one embodiment, the metabolic disease treatment agent may be applied without limitation as long as it exhibits a metabolic disease prevention and / or treatment effect. For example, the metabolic disease treatment agent may be a commercially available substance.

[0202] As metabolic diseases, individuals, expression levels, and mathematical formula 1 have been described above, detailed explanations are omitted.

[0203]

[0204] A composition and / or kit according to one aspect of the present specification may be used for predicting the onset of a metabolic disease, diagnosing a metabolic disease, predicting whether a metabolic disease will improve, or predicting the prognosis of a metabolic disease.

[0205] The information provision method according to one aspect of this specification may be used for the purpose of providing information for predicting the onset of a metabolic disease, for the purpose of providing information for diagnosing a metabolic disease, for the purpose of predicting whether a metabolic disease will improve, and for the purpose of providing information for predicting the prognosis of a metabolic disease.

[0206] A composition, kit, or information-providing method according to one aspect of the present disclosure can accurately predict metabolic diseases, allowing patients at high risk of developing metabolic diseases to receive appropriate management at an appropriate time, thereby delaying or preventing the onset of the disease. Furthermore, a composition, kit, or method according to one aspect of the present disclosure can be clinically used to diagnose metabolic diseases early and determine treatment by selecting the most appropriate treatment method.

[0207]

[0208] Hereinafter, the present invention will be described in more detail with examples. However, the following examples are provided solely to facilitate a better understanding of the present invention and are not intended to limit the scope of the present invention.

[0209]

[0210] 1. Discovery of miRNAs for predicting or diagnosing metabolic diseases

[0211] Obese patients before metabolic surgery (hereinafter, obese patients before metabolic surgery, BMI 30 kg / m 2 (above) and the normal group or the normal group after metabolic surgery (hereinafter, the normal group after metabolic surgery, BMI 18.5 kg / m 2 Exceeding 25kg / m 2 Samples of serum and adipose tissue were collected between the patient group and the normal group (below 10%), and biomarkers with different expression levels between the patient group and the normal group were selected. Afterwards, six miRNAs with different expression levels in both serum and adipose tissue samples were identified as biomarkers. Metabolic surgery is known to be the most effective treatment for severe obesity that is difficult to treat with general intervention methods, and metabolic surgery is known to be able to effectively improve metabolic diseases accompanying obesity. Therefore, miRNAs with different expression levels between patients before metabolic surgery and the normal group after metabolic surgery can be used to predict or diagnose metabolic diseases.

[0212] (1) miRNA analysis in serum samples

[0213] First, serum samples were collected from obese patients (n = 8) before metabolic surgery and normal controls (n = 21) after metabolic surgery, and biomarkers showing differences were analyzed. Specifically, the TPM values ​​of 2,886 miRNAs registered in the miRBase database were calculated for 29 samples. Subsequent analysis was conducted to determine whether any miRNAs were differentially expressed between the patient group before metabolic surgery, which can be considered obese, and the patient group after metabolic surgery, which can be considered normal controls. The TPMs calculated using the miRDeep tool were quantile normalized to remove systematic errors. T-tests and median tests were performed to compare the expression levels before and after metabolic surgery for each of the 2,886 miRNAs. miRNAs identified in more than half of the 29 samples, satisfying the T-test p-value <0.01 and the median-test p-value <0.05, were selected. As a result, seven miRNAs whose expression levels were significantly different between the obese group before metabolic surgery and the normal group after metabolic surgery were selected (see Figure 1).

[0214]

[0215] (2) miRNA analysis in adipose tissue samples

[0216] Adipose tissue samples were collected from patients (n = 8) before undergoing metabolic surgery and the normal control group (n = 5) to analyze biomarkers indicating differences between the groups. The analysis was performed using the same method as in the previous section, "(1) miRNA Analysis in Serum Samples." Specifically, the TPM values ​​of 2,886 miRNAs registered in the miRBase database were calculated for 13 samples. Subsequent analysis was performed to determine whether any miRNAs were differentially expressed between the patient group before metabolic surgery, which could be considered obese, and the patient group after metabolic surgery, which could be considered normal controls. The TPM values ​​calculated using the miRDeep tool were quantile normalized to eliminate systematic errors. T-tests and median tests were performed to compare the expression levels before and after metabolic surgery for each of the 2,886 miRNAs. miRNAs identified in more than half of the 13 samples, satisfying the T-test p-value <0.01 and the median-test p-value <0.05, were selected. As a result, 17 miRNAs whose expression levels were significantly different between the obese and normal groups before metabolic surgery were selected (see Figure 2).

[0217]

[0218] (3) Discovery of miRNAs for predicting or diagnosing metabolic diseases

[0219] Among the miRNAs selected in 1.(1) and 1.(2) above, six common miRNAs (miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p) were identified as biomarkers for predicting, diagnosing, and / or predicting the prognosis of metabolic diseases (see Fig. 3). Since severe obesity and metabolic diseases accompanying obesity, which are difficult to treat, can be effectively improved through metabolic surgery, the six miRNAs that showed significant differences in the samples before and after metabolic surgery identified in the above experiment can be utilized for predicting or diagnosing obesity or accompanying metabolic diseases.

[0220]

[0221] 2. Correlation analysis between the discovered miRNAs and clinical indicators of metabolic diseases.

[0222] In addition, the correlation between the 6 miRNAs discovered in the above '1. miRNA discovery for predicting or diagnosing metabolic diseases' and clinical indicators of metabolic diseases was analyzed. Specifically, among the 29 groups (obese group and normal group) of the above '1. miRNA discovery for predicting or diagnosing metabolic diseases', a correlation analysis was performed for each of the 2,886 miRNAs and clinical indicators (BMI (body mass index), FBS (Fasting Blood Sugar)) for the samples in the group with metabolic disease-related clinical indicators (BMI, FBS (Fasting Blood Sugar)). The correlation analysis was performed using Matlab's corr function, and if the analysis result R value (correlation coefficient) exceeds 0.5, it can be classified as a statistically significant correlation.

[0223] As a result, as disclosed in Table 2 below, miR-122-5p was confirmed to be statistically associated with the BMI clinical indicator.

[0224] Clinical Indicator miRNA Marker BMI FBShsa-miR-122-5p0.5337287040.353hsa-miR-93-5p0.153-0.281hsa-miR-106a-5p0.010-0.106hsa-miR-17-5p-0.030-0.267hsa-miR-20b-5p-0.164-0.181hsa-miR-20a-5p-0.182-0.299

[0225]

[0226] 3. Verification of the effectiveness of predicting metabolic diseases using the discovered miRNAs.

[0227] The six miRNA markers discovered in the above ‘1. Discovery of miRNAs for prediction or diagnosis of metabolic diseases’ were used to re-verify their effectiveness in predicting or diagnosing metabolic diseases (e.g., obesity).

[0228] First, we examined the predictive or diagnostic efficacy of using each of the six miRNAs as biomarkers. Specifically, we used the Linear Discriminant Analysis method to determine whether each of the six miRNAs could discriminate between the obese group (8 subjects) before metabolic surgery and the normal group (21 subjects) after metabolic surgery. The predicted values ​​were entered as whether the subjects were obese or normal, and the normalized TPM values ​​of the miRNAs in each subject (8 subjects in the obese group and 21 subjects in the patient group) were entered. Table 3 and Figures 4 through 9 below present the validation results using the six miRNAs alone.

[0229] Additionally, the present inventors developed a prediction or diagnostic model using a combination of six miRNAs using the Linear Discriminant Analysis method:

[0230] [Mathematical Formula 1]

[0231] [LDA score] = 1.867 * [hsa-miR-122-5p] + 1.186 * [hsa-miR-93-5p] -1.406 * [hsa-miR-17-5p] + 1.188 * [hsa-miR-20a-5p] -1.275 * [hsa-miR-20b-5p] -0.924 * [hsa-miR-106a-5p].

[0232] In the above mathematical formula 1, [hsa-miR-122-5p] represents the expression level of miR 122-5p, [hsa-miR-93-5p] represents the expression level of miR 93-5p, [hsa-miR-17-5p] represents the expression level of miR 17-5p, [hsa-miR-20a-5p] represents the expression level of miR 20a-5p, [hsa-miR-20b-5p] represents the expression level of miR 20b-5p, and [hsa-miR-106a-5p] represents the expression level of miR 106a-5p.

[0233] The performance of the prediction model developed using a combination of six miRNAs was evaluated using the 29 samples from the above "1. miRNA discovery for predicting or diagnosing metabolic diseases." The AUC of the six miRNA combination was 0.93, indicating that it could effectively and clearly distinguish metabolic disease groups from normal groups. Consequently, it was confirmed that the accuracy of predicting metabolic diseases was significantly higher when the six miRNA combination was used as a biomarker. Table 3 and Figure 10 show the validation results of the six miRNA combinations.

[0234] BiomarkerAUChsa-miR-122-5p + hsa-miR-93-5p + hsa-miR-17-5p + hsa-miR-20a-5p + hsa-miR-20b-5p + hsa-miR-106a-5p0.93hsa-miR-122-5p0.85hsa-miR-93-5p0.68hsa-miR-17-5p0. 82hsa-miR-20a-5p0.71hsa-miR-20b-5p0.8hsa-miR-106a-5p0.76hsa-miR-192-5p (reference)0.43

[0235]

[0236] Sequence number 1 (miR 122-5p sequence): 5'-UGGAGUGUGACAAUGGUGUUUG-3'

[0237] Sequence number 2 (miR 93-5p sequence): 5'-CAAAGUGCUGUUCGUGCAGGUAG-3'

[0238] Sequence number 3 (miR 17-5p sequence): 5'-CAAAGUGCUUACAGUGCAGGUAG-3'

[0239] Sequence number 4 (miR 20a-5p sequence): 5'-UAAAGUGCUUAUAGUGCAGGUAG-3'

[0240] Sequence number 5 (miR 20b-5p sequence): 5'-CAAAGUGCUCAUAGUGCAGGUAG-3'

[0241] Sequence number 6 (miR 106a-5p sequence): 5'-AAAAGUGCUUACAGUGCAGGUAG-3'

Claims

1. A composition for predicting or diagnosing a metabolic disease, comprising a preparation for measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p.

2. In paragraph 1, A composition for predicting or diagnosing metabolic diseases, wherein the agent for measuring the above expression level is a sense and antisense primer; or a probe; 3. In paragraph 1, A composition for predicting or diagnosing a metabolic disease, wherein the metabolic disease is at least one selected from the group consisting of obesity, hypertension, diabetes, insulin resistance syndrome, hyperlipidemia, and cardiovascular disease.

4. A kit for predicting or diagnosing metabolic disease, comprising a composition according to any one of claims 1 to 3.

5. In paragraph 4, A kit for predicting or diagnosing metabolic diseases, wherein the metabolic disease is at least one selected from the group consisting of obesity, hypertension, diabetes, insulin resistance syndrome, hyperlipidemia, and cardiovascular disease.

6. A step of measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample separated from a subject; and A method for providing information for predicting or diagnosing a metabolic disease, comprising: a step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease, when the expression level of the miR 122-5p is higher than that of the normal control group and the expression levels of miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p are lower than that of the normal control group.

7. A step of measuring the expression levels of miR 122-5p, miR 93-5p, miR 17-5p, miR 20a-5p, miR 20b-5p, and miR 106a-5p from a sample separated from a subject; and A method for providing information for predicting or diagnosing a metabolic disease, comprising: a step of determining that the subject has a metabolic disease or is likely to develop a metabolic disease if the LDA score calculated by the following [Mathematical Formula 1] is 1.5 or higher; [Mathematical Formula 1] [LDA score] = 1.867 * [hsa-miR-122-5p] + 1.186 * [hsa-miR-93-5p] -1.406 * [hsa-miR-17-5p] + 1.188 * [hsa-miR-20a-5p] -1.275 * [hsa-miR-20b-5p] -0.924 * [hsa-miR-106a-5p] (In the above mathematical formula 1, [hsa-miR-122-5p] represents the expression level of the above miR 122-5p, [hsa-miR-93-5p] represents the expression level of the above miR 93-5p, [hsa-miR-17-5p] represents the expression level of the above miR 17-5p, [hsa-miR-20a-5p] represents the expression level of the above miR 20a-5p, [hsa-miR-20b-5p] represents the expression level of the above miR 20b-5p, [hsa-miR-106a-5p] indicates the expression level of the above miR 106a-5p.) 8. In paragraph 6 or 7, A method for providing information for predicting or diagnosing a metabolic disease, wherein the sample is at least one selected from the group consisting of blood, serum, plasma, tissue, saliva, and urine.

9. In paragraph 6 or 7, A method for providing information for predicting or diagnosing a metabolic disease, wherein the metabolic disease is at least one selected from the group consisting of obesity, hypertension, diabetes, insulin resistance syndrome, hyperlipidemia, and cardiovascular disease.

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