System for predicting risk of coronary heart disease of type 2 diabetes patients and screening kit thereof

Abstract

The invention relates to a system for predicting the concurrent coronary heart disease risk of type 2 diabetic patients and a screening kit.The system comprises an index obtaining module and a column diagram early warning module, expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 are obtained through the index obtaining module, the column diagram early warning module outputs a corresponding prediction probability value according to a total score, and the larger the prediction probability value is, the higher the coronary heart disease risk is. The method can evaluate the risk of the current patient suffering from coronary heart disease, can screen early patients suffering from type 2 diabetes patients suffering from coronary heart disease in combination with a coronary heart disease diagnosis technology, and is beneficial to timely and effectively control the progress of coronary heart disease and reduce the disability and fatality rate.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and more specifically to a risk assessment system for type 2 diabetes patients complicated with coronary heart disease and a screening kit thereof. Background Art

[0002] Type 2 diabetes (scientific name: Type 2Diabetes Mellitus) is the most common type of diabetes, mainly caused by insufficient use of insulin or reduced efficiency. Although diabetes itself is less harmful to human health, long-term high blood sugar can cause damage to large and small blood vessels, and then affect the heart, brain, kidneys, peripheral nerves, eyes and feet. According to statistics from the World Health Organization, there are more than 100 complications of diabetes, making it one of the diseases with the most complications known so far.

[0003] More than half of the deaths from diabetes are caused by cardiovascular and cerebrovascular diseases. The relative risk of cardiovascular disease in diabetic patients is 2-4 times higher than that in non-diabetic patients, and coronary heart disease (scientific name: Coronary Heart Disease) is one of the most serious complications. Clinical data show that the incidence of coronary heart disease in diabetic patients is as high as 45%-70%. Compared with non-diabetic patients, the symptoms of coronary heart disease in patients with type 2 diabetes are usually atypical. Orcin Telli et al. pointed out in their study "Measuring the pain threshold and tolerance using electrical stimulation inpatients with Type II diabetes mellitus" that patients with type 2 diabetes often have obvious autonomic dysfunction, which leads to an increase in their pain threshold. Therefore, even if severe myocardial ischemia occurs, the symptoms of angina pectoris in these patients are often not obvious. This makes it easy to miss and misdiagnose complications of coronary heart disease when diagnosis is based solely on clinical symptoms.

[0004] Although coronary angiography is currently considered the gold standard for diagnosing coronary heart disease, this examination is invasive, expensive, complicated to operate, and may produce adverse reactions. It is not suitable for frequent implementation, which limits its application in early screening and long-term monitoring of diabetic coronary heart disease. Given that the type 2 diabetes population is large and prone to coronary heart disease, it has become an important task to find markers for predicting the risk of coronary heart disease in patients with type 2 diabetes. For example, patent CN118222695A found that the combination of oral Fusobacterium nucleatum and intestinal Lactobacillus can be used as a microbial marker for the diagnosis or prognosis of diabetic coronary heart disease. However, there is currently no officially recognized protein marker for predicting the risk of coronary heart disease in patients with type 2 diabetes. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a system for predicting the risk of coronary heart disease in patients with type 2 diabetes and a screening kit thereof, the purpose of which is to discover a group of proteins ESM1, BLVRA, HEXIM1 and SUMO3 that can be used as protein markers for assessing the risk of coronary heart disease in patients with type 2 diabetes. Compared with low-risk patients, the expression levels of BLVRA and SUMO3 in high-risk patients are significantly downregulated, and the expression levels of ESM1 and HEXIM1 are significantly upregulated, which is conducive to the early screening of patients with type 2 diabetes complicated by coronary heart disease, thereby solving the technical problem that the early symptoms of coronary heart disease in patients with type 2 diabetes complicated by coronary heart disease are atypical, and the complication of coronary heart disease is easily missed or misdiagnosed when diagnosis is made based on clinical symptoms.

[0006] To achieve the above object, according to one aspect of the present invention, a risk assessment system for coronary heart disease in patients with type 2 diabetes is provided, which includes an indicator acquisition module and a nomogram early warning module;

[0007] The indicator acquisition module is used to obtain the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in the protein markers of patients with type 2 diabetes, and submit them to the nomogram early warning module;

[0008] The nomogram early warning module includes an indicator scoring submodule, a total score calculation submodule and a probability prediction submodule;

[0009] The indicator scoring submodule is used to calculate the score corresponding to each indicator of the current patient according to the value of each indicator obtained by the indicator acquisition module, and submit it to the total score calculation submodule, wherein:

[0010] The higher the expression level of ESM1 or HEXIM1 obtained, the higher the score corresponding to its index;

[0011] The higher the expression level of BLVRA or SUMO3 obtained, the lower the score corresponding to its index;

[0012] The total score calculation submodule is used to accumulate the scores corresponding to the various indicators obtained by the indicator scoring submodule to obtain the total score of the current patient, and submit it to the probability prediction submodule;

[0013] The probability prediction submodule outputs a corresponding predicted probability value according to the total score. The larger the predicted probability value is, the higher the risk of the current patient developing coronary heart disease is.

[0014] Preferably, the indicator scoring submodule of the risk assessment system calculates the score corresponding to each indicator according to the following method:

[0015] When the relative expression range of ESM1 is [11.5, 16], the score interval corresponding to its index is [0, 45];

[0016] When the relative expression range of HEXIM1 is [9, 12], the score interval corresponding to its index is [0, 100];

[0017] When the relative expression range of BLVRA is [14.5, 8.5], the score interval corresponding to its index is [0, 82.5];

[0018] When the relative expression range of SUMO3 is [13.5, 8], the corresponding score interval of its index is [0, 30].

[0019] Preferably, in the risk assessment system, the probability prediction submodule outputs a corresponding prediction probability value interval of [0.01, 0.9] according to a total score range of [76, 135].

[0020] Preferably, the risk assessment system outputs a corresponding predicted probability value interval of [0.5, 0.9] according to a total score range of [114, 135].

[0021] Preferably, the risk assessment system outputs a corresponding predicted probability value interval of [0.7, 0.9] according to a total score range of [121, 135], indicating that the current patient belongs to a high-risk group for coronary heart disease.

[0022] Preferably, the risk assessment system outputs corresponding predicted probability values ​​of 0.1, 0.3, 0.5, 0.7 and 0.9 according to the total score ranges of 96-98, 107-109, 114-116, 121-123 and 133-135, respectively.

[0023] According to another aspect of the present invention, there is provided an application of a protein detection reagent in the preparation of a screening kit for type 2 diabetes patients complicated with coronary heart disease, wherein the detection reagent is a reagent for detecting the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in protein markers.

[0024] Preferably, in the application, the expression levels of ESM1 and HEXIM1 are upregulated, and the expression levels of BLVRA and SUMO3 are downregulated, indicating that the risk of coronary heart disease in patients with type 2 diabetes is increased.

[0025] Preferably, in the application, the reagent is a reagent for quantitatively detecting the protein levels of ESM1, BLVRA, HEXIM1 and SUMO3.

[0026] According to another aspect of the present invention, a screening kit for type 2 diabetes patients complicated with coronary heart disease is provided, which comprises reagents for detecting the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in protein markers.

[0027] Preferably, the reagents in the screening kit are reagents for quantitatively detecting the protein levels of ESM1, BLVRA, HEXIM1 and SUMO3.

[0028] Preferably, the reagents of the screening kit include one or more of the reagents used for detecting ESM1, BLVRA, HEXIM1 and SUMO3 protein levels by immunoblotting, enzyme-linked immunosorbent assay, mass spectrometry, radioimmunoassay, radioimmunodiffusion, immunoelectrophoresis, tissue immunostaining, immunoprecipitation analysis, complement fixation analysis, fluorescence activated cell sorting, mass analysis or protein microarray.

[0029] Preferably, the reagents in the screening kit include one or more reagents used for quantitatively detecting the protein levels of ESM1, BLVRA, HEXIM1 and SUMO3 by immunoblotting, enzyme-linked immunosorbent assay, or mass spectrometry.

[0030] In general, the above technical solution conceived by the present invention, compared with the prior art, can achieve the following beneficial effects due to the discovery that ESM1, BLVRA, HEXIM1 and SUMO3 can be used as protein markers for screening patients with type 2 diabetes complicated with coronary heart disease:

[0031] The risk assessment system for coronary heart disease in patients with type 2 diabetes provided by the present invention comprises an indicator acquisition module and a nomogram early warning module, wherein the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 are acquired through the indicator acquisition module, and the nomogram early warning module outputs a corresponding predicted probability value according to the total score, wherein the larger the predicted probability value is, the higher the risk of coronary heart disease in the current patient is, and the risk of coronary heart disease in patients with type 2 diabetes can be effectively assessed, and unnecessary invasive examinations for patients with low risk of coronary heart disease can be reduced; and the early stage patients with coronary heart disease in patients with type 2 diabetes can be screened in combination with the diagnostic technology of coronary heart disease, and the patients can be given corresponding treatment in time, thereby effectively delaying the development of coronary heart disease, and helping to reduce the disability and mortality rate of coronary heart disease in patients with type 2 diabetes. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Build a flow chart for the model;

[0033] Figure 2 Quantitative protein distribution diagram for low-risk group and high-risk group of patients with type 2 diabetes complicated with coronary heart disease;

[0034] Figure 3 The quantitative distribution diagram of peptide segments in the low-risk group and high-risk group of patients with type 2 diabetes complicated with coronary heart disease;

[0035] Figure 4 These are differentially expressed proteins screened from the low-risk group and high-risk group of patients with type 2 diabetes complicated with coronary heart disease;

[0036] Figure 5 is the principal component analysis diagram;

[0037] Figure 6 It is a quantitative heat map of differentially expressed proteins;

[0038] Figure 7 This is the ranking diagram of the importance of predicted proteins obtained by XGBoost screening;

[0039] Figure 8 is the relationship between the fitting error and log(λ);

[0040] Fig. 9 is the LASSO coefficient distribution diagram of differentially expressed proteins;

[0041] Fig.10 It is a box plot of protein marker ESM1 protein expression;

[0042] Fig.11 It is a box plot of protein marker BLVRA protein expression;

[0043] Fig.12 It is a box plot of the protein marker HEXIM1 protein expression;

[0044] Fig.13 It is a box plot of protein marker SUMO3 protein expression;

[0045] Fig.14 It is the receiver operating characteristic curve (ROC) diagram of the risk assessment model;

[0046] Fig.15 This is the confusion matrix diagram of the risk assessment model;

[0047] Fig.16 It is the calibration curve diagram of the risk assessment model;

[0048] Fig.17 It is the decision curve (DCA) diagram of the risk assessment model;

[0049] Fig.18 A nomogram for predicting the risk of coronary heart disease events in patients with type 2 diabetes. DETAILED DESCRIPTION

[0050] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0051] In the present invention, the term "type 2 diabetes" is the most common type of diabetes, which is mainly caused by insufficient use of insulin or reduced efficiency, resulting in increased blood sugar in the body. This chronic disease mainly occurs in adults and is also called adult-onset diabetes.

[0052] In the present invention, the term "coronary heart disease" is the full name of coronary atherosclerotic heart disease, sometimes also called ischemic heart disease. It refers to a heart disease caused by myocardial ischemia and hypoxia due to coronary atherosclerosis.

[0053] In the present invention, the term "complication" refers to the occurrence of another disease or symptom caused by one disease during its development, and the latter is the complication of the former.

[0054] In the present invention, the term "coronary artery Gensini score" is an indicator for evaluating the severity of coronary artery lesions. It analyzes coronary angiography images and calculates a total score based on the location of the lesion and the degree of stenosis. The total score reflects the overall severity of coronary artery disease. The higher the score, the more severe the coronary stenosis. The Gensini classification can better determine the patient's risk level and guide the subsequent treatment plan.

[0055] In the present invention, the term "protein marker" broadly refers to any detectable compound present in or derived from a biological sample, such as a protein, peptide, proteoglycan, glycoprotein, lipoprotein, etc.

[0056] The term "expression level" generally refers to the amount of a polynucleotide or amino acid product or protein in a biological sample. "Expression" generally refers to the process by which the information encoded by a gene is converted into a structure that exists and operates in a cell. Therefore, "expression" as used herein refers to transcription into polynucleotides, translation into proteins, or post-translational modifications to proteins. Fragments of transcribed polynucleotides, translated proteins, or post-translationally modified proteins are also considered to be products of expression, whether they are derived from transcripts produced by alternative splicing or degraded transcripts, or from post-translational modifications of proteins.

[0057] The present invention is based on the detection of biochemical indicators and coronary artery gensini scores of patients with type 2 diabetes, and through the proteomic difference analysis between the low-risk group and the high-risk group of patients with type 2 diabetes complicated with coronary heart disease, 28 important proteins are screened out from 167 differentially expressed proteins and sorted according to importance, and then the Lasso regression model is used for five-fold cross validation to screen out 22 core proteins from the 28 important proteins. The 22 core proteins are subjected to Logistic regression analysis in a random combination manner to construct a risk assessment model, and the optimal prediction combination is screened by the discrimination index AUC and the calibration index Brier score calculated by three-fold cross validation, that is, ESM1, BLVRA, HEXIM1 and SUMO3 are used as the best protein marker combination, and a risk assessment model for patients with type 2 diabetes complicated with coronary heart disease is established.

[0058] Based on this, the present invention provides a risk assessment system for type 2 diabetes patients complicated with coronary heart disease, which includes an indicator acquisition module and a nomogram early warning module;

[0059] The indicator acquisition module is used to obtain the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in the protein markers of patients with type 2 diabetes, and submit them to the nomogram early warning module;

[0060] The nomogram early warning module includes an indicator scoring submodule, a total score calculation submodule and a probability prediction submodule;

[0061] The indicator scoring submodule is used to calculate the score corresponding to each indicator of the current patient according to the value of each indicator obtained by the indicator acquisition module, and submit it to the total score calculation submodule, wherein:

[0062] The higher the expression level of ESM1 or HEXIM1 obtained, the higher the score corresponding to its index;

[0063] The higher the expression level of BLVRA or SUMO3 obtained, the lower the score corresponding to its index;

[0064] The total score calculation submodule is used to accumulate the scores corresponding to the various indicators obtained by the indicator scoring submodule to obtain the total score of the current patient, and submit it to the probability prediction submodule;

[0065] The probability prediction submodule outputs a corresponding predicted probability value according to the total score. The larger the predicted probability value is, the higher the risk of the current patient developing coronary heart disease is.

[0066] Furthermore, in some embodiments, the indicator scoring submodule calculates the score corresponding to each indicator according to the following method:

[0067] When the relative expression range of ESM1 is [11.5, 16], the score interval corresponding to its index is [0, 45];

[0068] When the relative expression range of HEXIM1 is [9, 12], the score interval corresponding to its index is [0, 100];

[0069] When the relative expression range of BLVRA is [14.5, 8.5], the score interval corresponding to its index is [0, 82.5];

[0070] When the relative expression range of SUMO3 is [13.5, 8], the corresponding score interval of its index is [0, 30].

[0071] The probability prediction submodule outputs a corresponding prediction probability value interval of [0.01, 0.9] according to the total score range of [76, 135].

[0072] Furthermore, according to the total score range of [114, 135], the corresponding predicted probability value interval of the output is [0.5, 0.9]; among them, according to the total score range of [121, 135], the corresponding predicted probability value interval of the output is [0.7, 0.9], indicating that the current patient belongs to the high-risk group for coronary heart disease.

[0073] More specifically: according to the total score ranges of 96-98, 107-109, 114-116, 121-123 and 133-135, the corresponding predicted probability values ​​of concurrent coronary heart disease are output as 0.1, 0.3, 0.5, 0.7 and 0.9 respectively.

[0074] In addition, the present invention provides an application of a reagent in preparing a screening kit for type 2 diabetes patients complicated with coronary heart disease, wherein the reagent is a reagent for detecting the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in protein markers.

[0075] Preferably, the reagent is a reagent for quantitatively detecting the protein levels of ESM1, BLVRA, HEXIM1 and SUMO3.

[0076] The application upregulates the expression levels of ESM1 and HEXIM1, and downregulates the expression levels of BLVRA and SUMO3, indicating that the risk of coronary heart disease in patients with type 2 diabetes is increased.

[0077] The present invention also provides a screening kit for type 2 diabetes patients complicated with coronary heart disease, which comprises reagents for detecting the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in protein markers; preferably, reagents for quantitatively detecting the protein levels of ESM1, BLVRA, HEXIM1 and SUMO3.

[0078] In some embodiments, the screening kit for type 2 diabetes patients complicated with coronary heart disease includes one or more of the reagents used for quantitatively detecting the content of ESM1, BLVRA, HEXIM1 and SUMO3 proteins by immunoblotting, enzyme-linked immunosorbent assay (ELISA), or mass spectrometry. Specifically, the reagents include binding agents that specifically bind to proteins encoded by ESM1, BLVRA, HEXIM1 and SUMO3; the protein binding agents include peptides, peptide mimetics, aptamers, spiegelmers, darpins, ankyrin repeat proteins, Kunitz-type domains, antibodies, single domain antibodies and / or monovalent antibody fragments, etc.

[0079] For example, when the content of ESM1, BLVRA, HEXIM1 and SUMO3 is detected by immunoblotting, the kit includes antibodies of four proteins: ESM1, BLVRA, HEXIM1 and SUMO3. When the content of ESM1, BLVRA, HEXIM1 and SUMO3 is detected by enzyme-linked immunosorbent assay, the kit includes standard samples of four proteins: ESM1, BLVRA, HEXIM1 and SUMO3. When the "absolute content" of ESM1, BLVRA, HEXIM1 and SUMO3 is detected by mass spectrometry detection technology PRM, the kit includes standard samples of quantitative representative peptides of four proteins: ESM1, BLVRA, HEXIM1 and SUMO3.

[0080] In the present invention, protein markers ESM1, BLVRA, HEXIM1 and SUMO3 also include genes and proteins encoded by them and homologs, mutations, and isoforms thereof. Full-length or unprocessed protein markers, protein markers in any form derived from processing in cells, and naturally occurring variants of protein markers (e.g., splice variants or allelic variants) are included. Among them, ESM1 (UniprotID: Q9NQ30), BLVRA (UniprotID: P53004), HEXIM1 (UniprotID: O94992), SUMO3 (UniprotID: P55854), UniprotID can be obtained at https: / / www.uniprot.org / .

[0081] The following are examples

[0082] The following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods in the examples without specifying specific conditions are usually carried out under conventional conditions or under conditions recommended by the manufacturer. The present invention is further described in detail below in conjunction with the accompanying drawings and examples, as follows:

[0083] Example 1: Constructing a risk assessment model for patients with type 2 diabetes mellitus complicated by coronary heart disease

[0084] The following are the inclusion and exclusion criteria for this study:

[0085] 1. Meet the diagnosis of type 2 diabetes;

[0086] 2. Undergo coronary angiography;

[0087] 3. Aged 18-85, regardless of gender;

[0088] 4. Volunteer to participate in this clinical study and sign the informed consent form.

[0089] Exclusion criteria:

[0090] 1. Acute infection within one month;

[0091] 2. Acute myocardial infarction occurred within one month;

[0092] 3. Acute heart failure;

[0093] 4. Pregnant or lactating women;

[0094] 5. Patients with malignant tumors or other diseases with a life expectancy of less than 2 years;

[0095] 6. Severe renal damage (eGFR ≤ 30%) and severe lung disease (acute or chronic respiratory failure and moderate or above pulmonary fibrosis, lung damage, etc.);

[0096] 7. People with mental illness or other inability to cooperate with the study and follow-up;

[0097] 8. Patients who have undergone coronary artery stent implantation or coronary artery bypass grafting.

[0098] According to the inclusion and exclusion criteria, a total of 51 patients with type 2 diabetes who underwent coronary angiography were included. Plasma samples were collected from these 51 patients with type 2 diabetes, and the basic information of the patients and related laboratory test indicators were recorded in detail when the samples were collected, including gender, age, height, weight, BMI, number of coronary artery stenoses, coronary artery gensini score, fasting blood glucose, triglycerides, total cholesterol, high-density lipoprotein, low-density lipoprotein, creatinine, uric acid and glomerular filtration rate.

[0099] The risk assessment criteria for coronary heart disease in type 2 diabetes are as follows: The risk assessment criteria for coronary heart disease are based on the coronary artery Gensini score to assess the severity of coronary artery lesions. Specifically, the median of the collected diabetic patients' Gensini score is used as the cutoff point. Patients with a score < the median are assessed to have a low risk of coronary heart disease, and patients with a score ≥ the median are assessed to have a high risk of coronary heart disease.

[0100] In this example, the median score of 30 of the 51 patients collected this time was used as the cutoff point, and patients with a score of <30 were classified as a low-risk group for concurrent coronary heart disease (n=25), and patients with a score of ≥30 were classified as a high-risk group for concurrent coronary heart disease (n=26). The demographic and clinical characteristics of the type 2 diabetes patients participating in this study were summarized, and the results are shown in Table 1.

[0101] Table 1 Demographic and clinical characteristics of patients with type 2 diabetes

[0102]

[0103]

[0104]

[0105] The letters in the table indicate “t: t-test; Z: Mann-Whitney test; 2 :Chi-square test; SD: standard deviation; M: median; Q 1 : The first quartile; Q 3 :third quartile".

[0106] From the collected type 2 diabetes patients, they were divided into a low-risk group and a high-risk group for coronary heart disease according to the coronary artery Gensini score, that is, 25 type 2 diabetes patients with low-risk coronary heart disease and 26 high-risk patients were studied by proteomics. The total sample was randomly divided into a training cohort and a validation cohort in a ratio of 7:3, where the training cohort was used to establish a risk assessment model, and the validation cohort was used to validate the constructed risk assessment model. The flowchart of this embodiment for constructing and validating a risk assessment model for coronary heart disease in type 2 diabetes patients is shown in the figure. Figure 1 As shown in the figure, the construction and verification of the risk assessment model are as follows:

[0107] 1. Sample collection and storage

[0108] Collection of patient plasma samples: venipuncture, collect 2 mL of peripheral blood in a purple blood collection tube (EDTA), gently invert to mix, and let stand for 30 minutes; within 60 minutes, centrifuge the blood collection tube at 2500×g for 10 minutes, transfer the upper plasma to a new centrifuge tube; divide the plasma sample into 100 μL per tube and mark the sample tubes; quickly freeze the divided plasma samples in a -20℃ refrigerator for short-term storage, or in a -80℃ refrigerator for long-term storage, and avoid repeated freezing and thawing of the samples.

[0109] 2. Experimental methods

[0110] This embodiment uses the next generation of label-free quantitative proteomics technology to complete the analysis. In the data independent acquisition (DIA) mode, the latest high-resolution mass spectrometry is used to simultaneously acquire peptide ion characteristics in terms of mass number and retention time. Compared with the traditional method of extracting a single ion for fragmentation analysis, the mass spectrometer in the DIA mode is set to a wide parent ion window for cyclic acquisition and simultaneous fragmentation of multiple peptide ions. The complete acquisition of all detectable protein peak information in the sample is achieved, so that a large number of samples can be analyzed with high repeatability. The DIA process provides an ideal platform for qualitative analysis of differentially expressed proteomes or quantitative analysis of proteomes of massive samples. The detection and analysis process steps are as follows:

[0111] 1) Sample preparation

[0112] The sample was enriched using superparamagnetic iron oxide nanoparticles. Take 20 microliters of sample, dilute with loading buffer (10mMTris-Cl, 1mM EDTA, 150mM KCl, 0.05% CHAPS), mix evenly with 1 mg of nanoparticle suspension, and incubate at 37°C for 1 hour. Wash the magnetic beads twice with loading buffer, and then wash the magnetic beads once with loading buffer without CHAPS (10mM Tris-Cl, 1mM EDTA, 150mM KCl). Adsorb the magnetic beads on the magnetic stand, discard the supernatant, and obtain the protein-enriched nanoparticles.

[0113] Lysis buffer (1% SDC / 100 mM Tris-HCl, pH=8.5 / 10 mM TCEP / 40 mM CAA) was added to the sample and incubated at 60°C for 30 min for reductive alkylation reaction. An equal volume of ddHO was added 2 O, dilute the SDC concentration to less than 0.5%, add 1 μg of trypsin, and incubate at 37°C with shaking overnight for enzyme digestion. The next day, add TFA to terminate the enzyme digestion, take the supernatant and desalt it with SDB-RPS desalting column, vacuum dry it and freeze it at -20°C for later use.

[0114] Description of reagents used in the experiment: Tris-HCl: tris(hydroxymethylaminomethane) hydrochloride; EDTA: ethylenediaminetetraacetic acid; KCl: potassium chloride; CHAPS: 3-[(3-cholamidopropyl)dimethylamino]propanesulfonic acid inner salt; TCEP: tris(2-carbonylethyl)phosphine hydrochloride; CAA: calcium acetylacetonate; ddH 2 O: double deionized water; SDC: sodium deoxycholate; TFA: trifluoroacetic acid; SDB-RPS: sulfonated styrene-vinylbenzene copolymer.

[0115] 2) Mass spectrometry

[0116] The peptide samples frozen at -20°C were taken for mass spectrometry detection. The mass spectrometry detection of the samples was performed using an UltiMate3000RSLCnano nanoliter liquid phase (Thermo) in series with a timsTOF Pro mass spectrometer (Bruker). The peptide samples were injected through an automatic sampler and bound to a C18 trapping column (75μm*2cm, 3μm particle size, pore size, Thermo), and then enters the analytical column (75μm*25cm, 1.6μm particle size, pore size, IonOpticks). An analytical gradient was established using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN). The flow rate of the analysis was set to 300nL / min. The mass spectrometer was used for data acquisition in diaPASEF mode. The capillary voltage was set to 1400V. The scanning range of the MS1 and MS2 spectra was set to 100-1700m / z. The ion mobility range was set to 0.6-1.6Vs / cm2. Accumulation time and ramp time were set to 100ms. According to the distribution law of mass-to-charge ratio-ion mobility, the diaPASEF acquisition window was set using timsControl software. The collision energy was set to increase linearly from 59eV at 1 / K0=1.6Vs / cm^2 to 20eV at 1 / K0=0.6Vs / cm^2 according to the ion mobility.

[0117] 3) Data analysis

[0118] ① Database search

[0119] DIA raw data were analyzed using the library-free mode of DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database downloaded from Uniprot. The search parameters were mainly set by default, and the key parameters are described as follows: the Precursor ion generation option was enabled for theoretical spectrum library prediction; Trypsin / P was used, and a maximum of 1 missed cleavage site was allowed; Carbamidomethyl (C) modification was set as a fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) were set as variable modifications; MS1 and MS2 mass tolerances were set to 15 ppm; MBR was enabled; Heuristic protein inference was enabled; FDR was set to 1%. Protein quantitative information was normalized using the MaxLFQ algorithm.

[0120] ②Data cleaning, filtering, conversion and filling

[0121] Contaminant proteins in the quantitative protein matrix were removed, and proteins with a missing ratio of more than 70% in the samples were filtered and logarithmically transformed. Then, missing values ​​were filled with values ​​representing a normal distribution near the detection limit of the mass spectrometer. To do this, we determined the mean and standard deviation of the actual intensity distribution, and then created a new distribution with a downward shift of 1.8 standard deviations and a width of 0.25 standard deviations. These values ​​were used to estimate the missing values ​​in the protein matrix. Finally, proteins with a missing ratio of more than 50% in both groups were removed, and the remaining proteins were used for subsequent analysis.

[0122] ③ Differential expression analysis

[0123] The protein quantitative data of the low-risk group and high-risk group of patients with type 2 diabetes complicated with coronary heart disease were compared, and the differentially expressed proteins between the two groups were screened by using the fold change (FC) and difference test (two-sided P < 0.05). The screening criteria were |log2FC| ≥ 0.58, p < 0.05.

[0124] The results and analysis are as follows:

[0125] (1) Using data independent acquisition (DIA) technology, we obtained plasma proteome profiles of 25 patients with type 2 diabetes and coronary heart disease at low risk and 26 patients at high risk. Using DIA technology, a total of 3,937 proteins and 42,323 peptides were quantified in the entire cohort. Figure 2 and Figure 3 shown.

[0126] (2) The expression of 167 proteins in patients with type 2 diabetes with low-risk coronary heart disease was significantly different from that in patients with high-risk coronary heart disease. Compared with the low-risk group, the expression of 146 proteins in the high-risk group was upregulated and the expression of 21 proteins was downregulated. Figure 4 shown.

[0127] (3) The results of principal component analysis (PCA) showed the intensity of 167 differentially expressed proteins, indicating a significant difference between patients with low risk of coronary heart disease and patients with high risk of type 2 diabetes. Figure 5 shown.

[0128] (4) A heat map was created using the expression levels of 167 differentially expressed proteins, showing that there were significant differences in the expression levels of differentially expressed proteins between patients with low risk of coronary heart disease and patients with high risk of type 2 diabetes. Figure 6 shown.

[0129] ④Key protein screening

[0130] The total samples were randomly divided into training cohort and validation cohort in a ratio of 7:3.

[0131] In the training cohort, the Extreme Gradient Boosting (XGBoost) machine learning model was used to screen out important proteins from the differentially expressed proteins and display their prediction importance ranking. The Lasso regression model was then used for five-fold cross validation to screen out N differential protein features from the important proteins screened by XGBoost; then, these N proteins were screened by three-fold cross validation, and the optimal prediction model was selected by the discrimination index AUC and the calibration index Brill score calculated by three-fold cross validation to select the optimal protein marker combination for constructing a logistic regression model. The differentially expressed proteins in the optimal combination are the disease-related protein markers.

[0132] ⑤Model construction and evaluation

[0133] Logistic regression and cross-validation were used to randomly select protein combinations from protein markers to construct multiple prediction models in the training cohort, and the optimal model was selected based on the area under the receiver operating characteristic curve (AUC) and Brill score. The optimal prediction model was then evaluated. The discrimination, calibration, and clinical utility performance of the model in the training cohort and validation cohort were evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curves in the training cohort and validation cohort, respectively. It is generally believed that when 0.90≤AUC<1.00, the model has excellent discrimination ability; when 0.75≤AUC<0.90, the model has good discrimination ability; when 0.60≤AUC<0.75, the model has certain discrimination ability, but it is not recommended; when AUC<0.60, the model has poor discrimination ability. All statistical analyses were performed using R (version 4.3.2) and Python (version 3.10.12).

[0134] The results are as follows:

[0135] In the training cohort, the Extreme Gradient Boosting (XGBoost) machine learning model was used to screen out 28 important proteins from 167 differentially expressed proteins and display their predicted importance ranking, such as Figure 7 shown.

[0136] The Lasso regression model was used for five-fold cross validation to screen out 22 core proteins from 28 important proteins. The screening process was as follows: Figure 8 and Fig. 9 The specific results are shown in Table 2.

[0137] Table 2 Lasso screening results

[0138]

[0139]

[0140] In the table, "labels" is the UniprotID of the protein, "coefs" is the coefficient of the protein, and "Intercept" is the constant term in the regression model.

[0141] Then, the 22 core proteins were randomly combined (the number of proteins did not exceed 5) to perform logistic regression analysis to construct a risk assessment model, and the optimal prediction model was screened by the discrimination index AUC and calibration index Brier score calculated by three-fold cross validation. The results showed that the prediction model of ESM1, BLVRA, HEXIM1 and SUMO3 combined had an AUC of 0.921 in the training set and an AUC of 0.917 in the test set, indicating that ESM1, BLVRA, HEXIM1 and SUMO3 can be used as a combination of protein markers to assess the risk of diabetic coronary heart disease and establish a risk assessment model, while the prediction model of Q9UGM3 (DMBT1) and P01709 (IGLV2-8) combined had an AUC of 0.832 in the training set and an AUC of 0.722 in the test set. The AUC of the test set was less than 0.75, and the model had a low discrimination ability and was not recommended. The analysis results of the prediction model constructed by combining ESM1, BLVRA, HEXIM1 and SUMO3 are shown in Table 3.

[0142] Table 3 Logistic regression model

[0143]

[0144] In the table, "Estimate" is the regression coefficient, "Std.Error" is the standard error of the regression coefficient, "z value" is the test statistic of the Z test on the regression coefficient, and "Pr(>|z)" is the P value of the Z test on the regression coefficient.

[0145] The expression levels of the four protein markers ESM1, BLVRA, HEXIM1 and SUMO3 in the two groups of people were plotted as box plots to visualize the expression differences of these four protein markers in the plasma of the low-risk group and the high-risk group of type 2 diabetic patients with coronary heart disease. The results are as follows Figure 10-13 The box plots showed that compared with the low-risk group of type 2 diabetic patients with coronary heart disease, the expression levels of BLVRA and SUMO3 were downregulated in the high-risk group, and the expression levels of ESM1 and HEXIM1 were upregulated in the high-risk group.

[0146] Example 2 Risk Assessment Model Validation

[0147] In the prior art, receiver operating characteristic (ROC) curves, confusion matrices, calibration curves and decision curves are often used to evaluate prediction models. In this example, the validation cohort is used to validate and evaluate the risk assessment model composed of protein markers ESM1, BLVRA, HEXIM1 and SUMO3; specifically, as follows:

[0148] ① Receiver operating characteristic curve (ROC) plot

[0149] Receiver operating characteristic curve, the horizontal axis is the false positive rate (1-specificity, 1-specificity), and the vertical axis is the true positive rate (sensitivity, sensitivity). Each point on the ROC line graph represents a sensitivity / 1-specificity pair corresponding to a specific decision threshold. A test with perfect discrimination (two result distributions do not overlap) has a ROC line graph through the upper left corner, where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). The theoretical line graph of a test that does not distinguish (the result distributions of the two groups are the same) is a 45° diagonal line from the lower left corner to the upper right corner. Most of the line graphs fall between these two extremes. Qualitatively, the closer the curve is to the upper left corner, the larger the area under the curve (Area Under Receiver Operating Characteristic Curve, AUC), and the higher the overall accuracy of the model.

[0150] It is generally believed that when 0.90≤AUC<1.00, the model has excellent discrimination ability; 0.75≤AUC<0.90, the model has good discrimination ability; 0.60≤AUC<0.75, the model has certain discrimination ability, but it is not recommended; AUC<0.60, the model has poor discrimination ability. The ROC curve was used to verify the risk assessment model based on the validation cohort. The results are as follows Fig.14 shown.

[0151] The ROC curve shows that the AUC (95% CI) of the training set is 0.921 (0.841-1), and the AUC (95% CI) of the test set is 0.917 (0.738-1). The ROC curve shows that the risk assessment model for type 2 diabetes patients with coronary heart disease has excellent discrimination ability and high model accuracy.

[0152] ② Confusion Matrix

[0153] The confusion matrix, also known as the error matrix, is a standard format for expressing accuracy evaluation. It is commonly expressed in a 2-row, 2-column matrix. Each row of the confusion matrix represents the predicted category, and the total number of each row represents the number of data predicted to be in that category; each column represents the true category of the data, and the total number of data in each column represents the number of data instances in that category. So after obtaining the confusion matrix of the model, you need to see how many observations are in the corresponding positions of the second and fourth quadrants. The more values ​​here, the better; conversely, the fewer observations appearing in the corresponding positions of the first and third quadrants, the better.

[0154] The risk assessment model was validated using a confusion matrix based on the validation cohort, and the results were as follows: Fig.15 As shown in the figure, the confusion matrix diagram shows the classification of the risk of coronary heart disease in patients with type 2 diabetes by the risk assessment model. The horizontal axis represents the actual category and the vertical axis represents the predicted category. The people in the upper left quadrant and the lower right quadrant are classified into the correct category. The confusion matrix diagram results show that the model sensitivity is 100% (6 / 6) and the specificity is 83.3% (5 / 6), indicating that the risk assessment model has good predictive ability.

[0155] ③Calibration curve

[0156] Calibration curves are often used to evaluate the consistency between the model's predicted probability and the actual probability of occurrence, also known as goodness of fit. Generally, the categories directly determined by the model (such as whether or not a person is ill) are called relative risk predictions; if it is assumed that everyone has a risk of illness, the difference is that the risk of illness is different, then the specific value of the risk of illness determined by the model, that is, the model's predicted probability, is called an absolute risk prediction. Calibration is an evaluation indicator for evaluating the accuracy of the model's absolute risk prediction. At present, the Hosmer-Lemeshow goodness of fit test method is commonly used to evaluate the calibration of binary classification models. The basic idea of ​​this method is to divide the observed samples into several groups according to the size of the predicted probability, and then calculate the observed frequency (the number of people who are actually ill in the group) and the predicted frequency (the number of people in the group predicted by the model to be ill) of each group, and finally calculate a fitting straight line, and use the fitting straight line to test the difference between the observed frequency and the predicted frequency. The calibration curve is the visualization of the results of the Hosmer-Lemeshow test. The higher the consistency between the model's predicted probability and the actual observation results, the better the calibration of the model.

[0157] The calibration curve is used to evaluate the calibration of the risk assessment model, that is, the consistency between the predicted probability of the outcome and the actual observed frequency of occurrence. The calibration curve results based on the validation cohort are as follows: Fig.16As shown, the dotted line is the ideal curve, the red line is the actual curve of the model, and the blue line is the curve after resampling calibration. The closer the calibration curve is to the ideal curve, the better the model effect. The results of the calibration curve show that the predicted probability and observed probability of the risk assessment model provided by the present invention are consistent.

[0158] ④Decision curve

[0159] The decision curve is a method used to evaluate the clinical application value of a prediction model. It draws a decision curve to compare the clinical benefits of different prediction models at different treatment decision thresholds, helping medical researchers and clinicians make the best choice between different prediction models to achieve better clinical prediction results. In the figure, the probability threshold is the horizontal axis and the net benefit rate is the vertical axis. The red line is the decision curve, which is the change in the net benefit rate brought about by the intervention based on the prediction results of the logistic model with the probability threshold, while the bottom black horizontal line None represents the net benefit rate of 0 when no treatment is performed, and the gray curve All represents the change in the net benefit rate of all treatments with the probability threshold. Within a certain range, the higher the net benefit rate of the model, the greater its clinical utility.

[0160] The decision curve is used to evaluate the clinical utility of the model. The net rate of return reflects the clinical value of the model. The larger the net rate of return, the better the model effect. The decision curve results based on the validation cohort are as follows: Fig.17 As shown in the decision curve diagram, within a certain range, the net rate of return of the risk assessment model is higher and higher than both all treatment and no treatment options, which proves that the risk assessment model provided by the present invention has greater clinical utility.

[0161] Example 3 Risk Assessment Model Visualization

[0162] The nomogram is used to visualize the results of the risk assessment model in Example 1 to facilitate the prediction of the risk of coronary heart disease. The results are as follows: Fig.18 The individual scores (Points) corresponding to each protein marker at different values ​​are summed up to get the total score (Total Points), which is then converted to the Risk score, which is the probability of coronary heart disease events in the type 2 diabetes patient.

[0163] Specifically, in the nomogram, the relative expression range of ESM1 was 11.5-16, corresponding to 0-45.5 in the score scale; the relative expression range of BLVRA was 8.5-14.5, corresponding to 0-82 in the score scale; the relative expression range of HEXIM1 was 9-12, corresponding to 0-100 in the score scale; the relative expression range of SUMO3 was 8-13.5, corresponding to 0-31.5 in the score scale;

[0164] According to the total score range of [76, 135], the corresponding predicted probability value interval is [0.01, 0.9]. The larger the predicted probability value, the higher the risk of coronary heart disease in the current patient. Specifically:

[0165] The total score scale of 96-98, 107-109, 114-116, 121-123 and 133-135 corresponds to the probability of coronary heart disease events in patients with type 2 diabetes of 0.1, 0.3, 0.5, 0.7 and 0.9 respectively. Fig.18 It can be seen that the four markers combined to predict the risk of coronary heart disease in patients with type 2 diabetes can reach an upper limit of 0.9, and the risk assessment accuracy is relatively high.

[0166] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A risk assessment system for coronary heart disease in patients with type 2 diabetes, characterized in that: Including indicator acquisition module and nomogram warning module; The indicator acquisition module is used to obtain the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in the protein markers of patients with type 2 diabetes, and submit them to the nomogram early warning module; The nomogram early warning module includes an indicator scoring submodule, a total score calculation submodule and a probability prediction submodule; The indicator scoring submodule is used to calculate the score corresponding to each indicator of the current patient according to the value of each indicator obtained by the indicator acquisition module, and submit it to the total score calculation submodule, wherein: The higher the expression level of ESM1 or HEXIM1 obtained, the higher the score corresponding to its index; The higher the expression level of BLVRA or SUMO3 obtained, the lower the score corresponding to its index; The total score calculation submodule is used to accumulate the scores corresponding to the various indicators obtained by the indicator scoring submodule to obtain the total score of the current patient, and submit it to the probability prediction submodule; The probability prediction submodule outputs a corresponding predicted probability value according to the total score. The larger the predicted probability value is, the higher the risk of the current patient developing coronary heart disease is.

2. The risk assessment system according to claim 1, characterized in that: The indicator scoring submodule calculates the score corresponding to each indicator according to the following method: When the relative expression range of ESM1 is [11.5, 16], the score interval corresponding to its index is [0, 45]; When the relative expression range of HEXIM1 is [9, 12], the score interval corresponding to its index is [0, 100]; When the relative expression range of BLVRA is [14.5, 8.5], the score interval corresponding to its index is [0, 82.5]; When the relative expression range of SUMO3 is [13.5, 8], the corresponding score interval of its index is [0, 30].

3. The risk assessment system according to claim 2, characterized in that: The probability prediction submodule outputs a corresponding prediction probability value interval of [0.01, 0.9] according to the total score range of [76, 135].

4. The risk assessment system according to claim 3, characterized in that: According to the total score range of [114, 135], the corresponding output prediction probability value interval is [0.5, 0.9].

5. The risk assessment system according to claim 4, characterized in that: According to the total score range of [121, 135], the corresponding predicted probability value interval of the output is [0.7, 0.9], indicating that the current patient belongs to the high-risk group for coronary heart disease.

6. Use of a protein detection reagent in preparing a screening kit for type 2 diabetes patients complicated with coronary heart disease, characterized in that: The detection reagent is a reagent for detecting the expression levels of ESM1, BLVRA, HEXIM1 and SUMO3 in protein markers.

7. The use according to claim 6, characterized in that The expression levels of ESM1 and HEXIM1 are upregulated, and the expression levels of BLVRA and SUMO3 are downregulated, indicating that the risk of coronary heart disease in patients with type 2 diabetes is increased.

8. A screening kit for type 2 diabetes patients complicated with coronary heart disease, characterized in that: Includes reagents for detecting expression levels of protein markers ESM1, BLVRA, HEXIM1, and SUMO3.

9. The screening kit according to claim 8, characterized in that The reagent is a reagent for quantitatively detecting the protein levels of ESM1, BLVRA, HEXIM1 and SUMO3.

10. The screening kit according to claim 9, characterized in that The reagents include one or more of the reagents used for detecting ESM1, BLVRA, HEXIM1 and SUMO3 protein levels by immunoblotting, enzyme-linked immunosorbent assay, mass spectrometry, radioimmunoassay, radioimmunodiffusion, immunoelectrophoresis, tissue immunostaining, immunoprecipitation analysis, complement fixation analysis, fluorescence activated cell sorting, mass analysis or protein microarray.

Images