Method for estimating a risk for a subject suffering from coronary artery disease, analyzer and kit thereof

Abstract

A method for estimating a risk for a subject suffering from coronary artery disease is provided, including measuring biomarker content: cholesterol, triglycerides, LDL, HDL, miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or a combination thereof from the subject; and calculating the same biomarker content from a negative control and the sample by logistic regression, the subject is estimated having the risk of coronary artery disease when the biomarker content of the subject is higher or lower than that of the negative control. A kit of estimating a risk for a subject suffering from coronary artery disease is also provided, including at least one agent for identifying the biomarker content as above mentioned from the subject, thereby accurately assessing risk.

Description

[Technical Field]

[0001] This invention relates to an assessment method, its analyzer and kit, and more particularly to a method, its analyzer and kit for assessing an individual's risk of developing coronary heart disease. [Previous Technology]

[0002] Coronary artery disease (CAD) is a cardiovascular disease that is prone to occur during seasonal changes. The main cause of CAD is myocardial ischemia due to the blockage of the coronary arteries that supply oxygen to the heart by atherosclerosis. Because of the lack of accurate predictive markers, it remains a serious unresolved issue in clinical practice.

[0003] Therefore, there is a need to improve the existing technology in terms of how to provide accurate prediction labels. [Summary of the Invention]

[0004] One embodiment of this disclosure provides a method for assessing an individual's risk of developing coronary heart disease, comprising: measuring the levels of blood biochemicals in an individual's sample, the blood biochemicals including triglycerides (TG), high-density lipoprotein (HDL), or combinations thereof; and comparing the levels of the same blood biochemicals in a negative control group with those in the sample, wherein when the triglyceride level in the sample is higher than the triglyceride level in the negative control group, and / or when the HDL level in the sample is lower than the triglyceride level in the negative control group, the individual is assessed as having a risk of developing coronary heart disease.

[0005] In some embodiments, the negative control group is taken from a group of individuals known not to have coronary heart disease.

[0006] In some embodiments, the sample is blood, ascites, urine, feces, or saliva.

[0007] Another embodiment of this disclosure provides a method for assessing an individual's risk of developing coronary heart disease, comprising: measuring the levels of blood biochemicals in an individual's sample, the blood biochemicals including total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, or combinations thereof; measuring the expression levels of at least two microRNAs in the individual's sample, the at least two microRNAs including miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof; and generating at least one first ratio, the at least one first ratio being generated from the expression levels of at least two microRNAs in the sample using the following formula: = First ratio A Equation (1); = First ratio B Equation (2); = First ratio C Equation (3); = First ratio D Equation (4); = First ratio E Equation (5); = First ratio F Equation (6); = First ratio G Equation (7); = First ratio H Equation (8); = First ratio I Equation (9); or = First ratio J Equation (10); The positive and negative control groups are compared using at least one second ratio obtained from the levels of the same blood biochemical substances and at least two of the same microRNAs as the samples. A logistic regression model is then used to generate a negative control score for the negative control group. At least one second ratio is generated by the following formula: = Second ratio A Equation (1-1); = Second ratio B Equation (2-1); = Second ratio C Equation (3-1); = Second ratio D Equation (4-1); = Second ratio E Equation (5-1); = Second ratio F Equation (6-1); = Second ratio G Equation (7-1); = Second ratio H Equation (8-1); = Second ratio I Equation (9-1); or = Second ratio J Equation (10-1); and The levels of blood biochemical substances in an individual's sample are compared with at least one first ratio using a model formula to generate a sample score. An individual is assessed as having a risk of coronary heart disease when the sample score of the first ratio B, first ratio E, first ratio F, first ratio G, first ratio J, or a combination thereof is greater than the negative control score of the second ratio B, second ratio E, second ratio F, second ratio G, second ratio J, or a combination thereof; or when the sample score of the first ratio A, first ratio C, first ratio D, first ratio H, first ratio I, or a combination thereof is less than the negative control score of the second ratio A, second ratio C, second ratio D, second ratio H, second ratio I, or a combination thereof.

[0008] In some embodiments, the positive control group is taken from a group of individuals known to have coronary heart disease, and the negative control group is taken from a group of individuals known not to have coronary heart disease.

[0009] In some embodiments, the sample is blood, ascites, urine, feces, or saliva.

[0010] In some embodiments, at least two of the microRNAs are seven microRNAs: miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, and miR-484, wherein at least one first ratio is ten first ratios: first ratio A, first ratio B, first ratio C, first ratio D, first ratio E, first ratio F, first ratio G, first ratio H, first ratio I, and first ratio J, wherein at least one second ratio is ten second ratios: second ratio A, second ratio B, second ratio C, second ratio D, second ratio E, second ratio F, second ratio G, second ratio H, second ratio I, and second ratio J.

[0011] In some embodiments, miR-21 includes miR-21-5p, miR-22 includes miR-22-3p, miR-142 includes miR-142-5p, miR-146b includes miR-146b-5p, miR-222 includes miR-222-3p, or miR-425 includes miR-425-5p.

[0012] In some embodiments, the model formula is: Score = -3.191 + (1.550 * miR-484 / miR-222) - (1.769 * miR-425 / miR-22) + (0.681 * miR-22 / miR-142) + (0.748 * miR-484 / miR-425) + (2.573 * miR-146b / miR-484) - (0.459 * miR-146b / miR-22) + (1.329 * miR-222 / miR-22) + (0.211 * miR-21 / miR-146b) - (0.982 * miR-22 / miR-484) - (0.960 * miR-21 / miR-22) - (0.273 * Total cholesterol) + (3.375 * triglycerides) + (0.445 * high-density lipoprotein) + (0.706 * low-density lipoprotein) + (3.268 * total cholesterol / high-density lipoprotein) Equation (11).

[0013] Another embodiment of this disclosure provides a kit for assessing whether an individual suffers from coronary heart disease, comprising: at least one first reagent, the at least one first reagent being used to identify the content of at least one blood biochemical substance in an individual's sample, the at least one blood biochemical substance comprising total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, or a combination thereof.

[0014] In some embodiments, the kit further includes at least one second reagent for identifying the amount of microRNA expression in an individual's sample, wherein the microRNA includes miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof.

[0015] In some embodiments, at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-484 and miR-222; at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-425 and miR-22; at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-22 and miR-142; at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-484 and miR-425; At least one second reagent comprises multiple second reagents for identifying microRNAs miR-146b and miR-484 in the sample; at least one second reagent comprises multiple second reagents for identifying microRNAs miR-146b and miR-22 in the sample; at least one second reagent comprises multiple second reagents for identifying microRNAs miR-222 and miR-22 in the sample; at least one second reagent comprises multiple second reagents for identifying microRNAs miR-21 and miR-146b in the sample; at least one second reagent comprises multiple second reagents for identifying microRNAs miR-22 and miR-484 in the sample; or at least one second reagent comprises multiple second reagents for identifying microRNAs miR-21 and miR-22 in the sample.

[0016] In some embodiments, miR-21 includes miR-21-5p, miR-22 includes miR-22-3p, miR-142 includes miR-142-5p, miR-146b includes miR-146b-5p, miR-222 includes miR-222-3p, or miR-425 includes miR-425-5p.

[0017] In some embodiments, at least one second reagent comprises a primer pair, a probe, or a combination thereof, wherein at least one first reagent comprises a primer pair, a probe, or a combination thereof.

[0018] Another embodiment of this disclosure provides an analyzer for assessing an individual's risk of developing coronary heart disease, comprising: a detection device configured to detect the content of at least one blood biochemical substance in an individual's sample, the blood biochemical substance including triglycerides, high-density lipoprotein, or a combination thereof; a calculation device configured to calculate the content of the blood biochemical substance, including comparing the content of the same blood biochemical substance in a negative control group with that in the sample, and obtaining a calculation result; and a result output device configured to output the calculation result, wherein when the triglyceride content of the sample is higher than the triglyceride content of the negative control group, and / or when the high-density lipoprotein content of the sample is lower than the triglyceride content of the negative control group, the individual is assessed as having a risk of developing coronary heart disease.

[0019] In some embodiments, the detection device is further configured to detect the expression levels of at least two microRNAs in a sample of an individual, the at least two microRNAs including miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof, wherein the computing device further generates at least one first ratio, the at least one first ratio being generated from the expression levels of the at least two microRNAs in the sample using the following formula: = First ratio A Equation (1); = First ratio B Equation (2); = First ratio C Equation (3); = First ratio D Equation (4); = First ratio E Equation (5); = First ratio F Equation (6); = First ratio G Equation (7); = First ratio H Equation (8); = First ratio I Equation (9); or = First ratio J Equation (10); The positive and negative control groups are compared using at least one second ratio obtained from the levels of the same blood biochemical substances and at least two of the same microRNAs as the samples. A logistic regression model is then used to generate the model formula. The negative control group generates a negative control score, where at least one second ratio is generated by the following formula: = Second ratio A Equation (1-1); = Second ratio B Equation (2-1); = Second ratio C Equation (3-1); = Second ratio D Equation (4-1); = Second ratio E Equation (5-1); = Second ratio F Equation (6-1); = Second ratio G Equation (7-1); = Second ratio H Equation (8-1); = Second ratio I Equation (9-1); or = Second ratio J Equation (10-1); and The content of blood biochemical substances in an individual's sample is compared with at least one first ratio using a model formula to generate a sample score. The sample score and the negative control score are calculated as a result. A result output device is used to output the calculation result. When the sample score of the first ratio B, first ratio E, first ratio F, first ratio G, first ratio J or a combination thereof is greater than the negative control score of the second ratio B, second ratio E, second ratio F, second ratio G, second ratio J or a combination thereof, the individual is assessed as having a risk of coronary heart disease. Or when the sample score of the first ratio A, first ratio C, first ratio D, first ratio H, first ratio I or a combination thereof is less than the negative control score of the second ratio A, second ratio C, second ratio D, second ratio H, second ratio I or a combination thereof, the individual is assessed as having a risk of coronary heart disease.

[0020] In some embodiments, the detection device is further configured to detect an individual’s total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein.

Implementation Method

[0021] To make the description of this disclosure more detailed and complete, illustrative descriptions of embodiments and specific examples of this disclosure are provided below. However, this is not the only form of implementing or using the specific examples of this disclosure. The various embodiments disclosed below can be combined or substituted with each other where advantageous, and other embodiments can be added to one embodiment without further description or explanation. In the following description, many specific details will be set forth in detail to enable the reader to fully understand the following embodiments. However, the embodiments of this disclosure can also be practiced without such specific details.

[0022] In addition, spatial relative terms, such as "down" and "up," are used to conveniently describe the relative relationship of one element or feature to other elements or features in the diagram. These spatial relative terms are intended to encompass different orientations of the device during use or operation, in addition to those shown in the diagram. The device may be positioned otherwise (e.g., rotated 90 degrees or other orientations), and the spatial relative descriptions used herein may be interpreted accordingly.

[0023] In this document, unless otherwise specified in the text, the words “a” and “the” may refer to one or more. It will be further understood that the words “comprising,” “including,” “having,” and similar terms used herein refer to the features, regions, integers, steps, operations, elements, and / or components described herein, but do not exclude one or more other features, regions, integers, steps, operations, elements, components, and / or groups thereof described or additionally described herein.

[0024] In this article, "coronary heart disease" is also referred to as coronary artery heart disease.

[0025] In some embodiments, when a patient begins to experience chest tightness, chest pain, or abnormal exercise electrocardiogram (ECG), patients who test positive through screening using some embodiments of this disclosure can undergo further testing, such as myocardial perfusion scanning, coronary angiography, and exercise ECG. If coronary artery disease is diagnosed through the above tests, further treatment including medication, cardiac catheterization, and / or coronary artery bypass surgery is administered.

[0026] Pharmacological treatment for coronary heart disease includes the use of aspirin, nitroglycerin, beta-blockers, lipid-lowering agents, angiotensin-converting enzyme inhibitors, and calcium channel blockers.

[0027] Cardiovascular catheterization: For patients with unstable angina, cardiovascular catheterization can be performed to improve their quality of life by opening up the coronary arteries.

[0028] Coronary artery bypass surgery: For areas where the coronary arteries are completely blocked, the original blood vessel is replaced by a vein in the thigh or the internal mammary artery to provide coronary blood flow.

[0029] In other embodiments of this disclosure, an application of a test reagent in the preparation of a kit (reagent kit) for assessing the risk of coronary heart disease is provided, wherein the test reagent comprises triglycerides, low-density lipoprotein, high-density lipoprotein, or a combination thereof.

[0030] In some embodiments, the application further includes at least one microRNA selected from the group consisting of miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484 and combinations thereof.

[0031] In some embodiments, miR-21 is hsa-miR-21-5p (e.g., SEQ ID NO: 1); miR-22 is hsa-miR-22-3p (e.g., SEQ ID NO: 2); miR-142 is hsa-miR-142-5p (e.g., SEQ ID NO: 3); miR-146b is hsa-miR-146b-5p (e.g., SEQ ID NO: 4); miR-222 is hsa-miR-222-3p (e.g., SEQ ID NO: 5); miR-425 is hsa-miR-425 (e.g., SEQ ID NO: 6); and miR-484 is hsa-miR-484 (e.g., SEQ ID NO: 7). The miniRNA is derived from humans.

[0032] In some embodiments, the kit may be in the form of a reagent kit, and the kit may further include commonly used reagents for PCR reactions, such as primers or probes for detecting miniRNAs, buffers, deoxyribonucleotide triphosphates (dNTPs), magnesium chloride, purified water, and Taq polymerase. The kit may further include standards or controls.

[0033] In some embodiments, the probe or lead may be fixed to a solid carrier, such as a wafer.

[0034] In some other embodiments, the test kit can be used to detect coronary heart disease.

[0035] In some embodiments, the detection kit may be in the form of a reagent kit and may include commonly used reagents for PCR reactions, such as buffers, dNTPs, MgCl2, purified water, and Tag enzymes. It may also contain standards and / or controls.

[0036] In some embodiments of the detection kit, the probes or leads may be fixed to a solid carrier, such as a wafer.

[0037] In some embodiments, the specificity and accuracy of detecting coronary heart disease are obtained using the receiver operating characteristic curve (ROC curve). For example, the receiver operating characteristic curve is plotted using the software "prism", and the input data is the expression level of normalized microRNA. In the calculation part, preset values ​​are used. Then, the value corresponding to the maximum likelihood ratio is selected as the cut-off value. The sensitivity, specificity and accuracy are obtained from the cut-off value.

[0038] In addition, in some embodiments, the method includes testing different combinations of blood biochemical substances and / or microRNAs, performing numerical calculations on the content of each blood biochemical substance and / or the performance of the microRNA, and establishing a model formula using a multivariate logistic regression analysis method (e.g., logistic regression analysis) to assess the risk of coronary heart disease.

[0039] In some embodiments, the model formula may fluctuate depending on the number of training samples. However, the key to this disclosure lies in identifying the crucial blood biochemical substances and microRNAs. Therefore, the blood biochemical substances and microRNAs disclosed in some embodiments of this disclosure are not limited to a specific model formula.

[0040] The following examples illustrate the method for assessing an individual’s risk of coronary heart disease in more detail. However, they are only for illustrative purposes and are not intended to limit the scope of the present disclosure. The scope of protection of the present disclosure shall be defined by the appended claims.

[0041] Although the method disclosed herein is described below using a series of operations or steps, the order in which these operations or steps are shown should not be construed as a limitation of this disclosure. For example, some operations or steps may be performed in a different order and / or simultaneously with other steps. Furthermore, it is not necessary to perform all illustrated operations, steps, and / or features to achieve the implementation of this disclosure. In addition, each operation or step described herein may include several sub-steps or actions.

[0042] For clarity, features and elements that are known in the field and are not essential for understanding the principles described will be omitted.

[0043] Please refer to Figure 1, which illustrates a flowchart for screening microRNAs associated with coronary heart disease.

[0044] Method 100 begins with step 102, collecting plasma samples from patients. Because this group of patients has a high incidence of coronary heart disease, the differences in blood biochemical substances and microRNA expression levels between patients without and with coronary heart disease are compared.

[0045] Next, step 104 involves extracting total microRNA from the plasma sample. MicroRNAs are very stable in human tissue and cell samples and are not easily degraded. They can be detected in many bodily fluids such as blood, saliva, and urine. Some embodiments of this disclosure involve separating plasma from a patient's blood sample and then detecting blood biochemicals and microRNAs in the plasma.

[0046] The "micro ribosomal nucleic acid (miRNA)" described in this article is also known as small ribosomal nucleic acid, mini ribosomal nucleic acid, or small molecule ribosomal nucleic acid. Micro ribosomal nucleic acids are a small non-coding family composed of 19 to 25 nucleotides. They regulate gene expression by inhibiting the translation of messenger ribosomal nucleic acid (mRNA) or degrading messenger ribosomal nucleic acid after sequence-specific recognition.

[0047] The next step 106 is to reverse transcribe the total microRNA into complementary deoxyribonucleic acid (cDNA).

[0048] The next step, 108, involves performing a microRNA array experiment, where the reverse-transcribed cDNA is detected using a microRNA chip to assess the performance of the detectable microRNAs covered by the chip. For example, the TaqMan® Array Human MicroRNA A Cards v2.0 chip can be used to detect the performance of nearly three thousand human microRNAs.

[0049] Following this, step 110 involves data analysis and selection of candidate blood biochemical substances and genes. After analyzing the correlation between blood biochemical tests and coronary heart disease, substances exhibiting higher AUC levels between patients without and with coronary heart disease are selected as candidate blood biochemical substances. Based on the results of biochemical tests and microRNA array experiments, specific blood biochemical substances and microRNAs showing significant differences in content and expression levels between patients without and with coronary heart disease can be identified. For example, some specific blood biochemical substances and / or microRNAs show higher expression levels in samples from patients with coronary heart disease, while others show lower expression levels; these specific blood biochemical substances and / or microRNAs can serve as candidate blood biochemical substances and genes for detecting coronary heart disease.

[0050] Following this, step 112 of method 100 involves validating candidate blood biochemical substances and / or genes. These candidate blood biochemical substances and / or genes are further validated, for example, by performing biochemical tests to measure the levels of these individual candidate blood biochemical substances between non-coronary artery disease subjects and coronary artery disease patients; and by performing real-time PCR quantitative experiments to measure the expression levels of these individual candidate genes between non-coronary artery disease subjects and coronary artery disease patients. Additionally, validation is performed using samples from new subject populations.

[0051] Preparation Example

[0052] Subject Specimen Collection

[0053] Blood biochemical substances and total miRNA were extracted from the plasma sample of the subject in a conventional manner, and the total miRNA was reverse transcribed into complementary deoxyribonucleic acid (cDNA).

[0054] Performing blood biochemical substance array

[0055] After analyzing the correlation between biochemical blood tests and coronary heart disease, in addition to having a higher AUC as a candidate blood biochemical substance between patients without coronary heart disease and patients with coronary heart disease, if the AUC can be increased by combining with microRNA, it can also be used as a candidate blood biochemical substance, such as triglycerides, low-density lipoprotein and high-density lipoprotein.

[0056] Perform reverse transcription and mini ribonucleic acid array

[0057] After confirming that the quality of the total microRNA extracted from the sample meets the standards, a microRNA array experiment is performed. The steps of the array experiment include: taking 600 nanograms (ng) of total microRNA sample from the sample, performing reverse transcription (RT) according to the steps provided by the TaqMan MicroRNA Reverse Transcriptase manufacturer, and then performing a microRNA array experiment on the reverse transcription product (RT product, i.e., cDNA) according to the steps provided by the TaqMan® Array Human MicroRNA A Cards v2.0 manufacturer.

[0058] Data analysis and confirmation of candidate genes

[0059] Two methods were used for data normalization. The first method used RNU6 (U6 small nuclear RNA) for normalization. This method uses the CT value of RNU6 in the microRNA array as a reference point and converts it into an expression value using the formula 2 - ∆CT, where ∆CT is "the CT value of the target microRNA minus the CT value of RNU6". The second method uses the average CT value of all detected microRNAs in the microRNA array as a reference point and converts it into an expression value using the formula 2 - ∆CT, where ∆CT is "the CT value of the target microRNA minus the average CT value".

[0060] Next, the obtained data is used to perform cluster analysis and calculate the fold change using R language. The "plot" function of the "gplots" suite in R is used for plotting. The horizontal axis (x-axis) represents the fold change or difference, which is calculated by averaging the data from each group. The group with coronary artery disease (CAD) is used as the experimental group, and the group without CAD (non-CAD) is used as the control group. The fold change or difference is obtained by dividing the data of the experimental group (CAD) by the data of the control group (non-CAD). The vertical axis (y-axis) represents the p-value after the t-test is processed by -log10. The t-test is performed using the default "t.test" function in R language.

[0061] Samples from four subjects were used in a microRNA array experiment. Samples were collected from all four subjects before they were diagnosed with coronary heart disease (negative control) and after they were diagnosed (positive control). Table 1 below shows the characteristics of these four patients.

[0062] Table 1

[0063] After analyzing the data of the miniRNA arrays of the above 4 subjects, 7 miniRNA genes were selected: miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, and miR-484.

[0064] To further validate the results of the initial screening, new subjects were used for validation. Samples were collected from 160 subjects, and plasma was separated. Total microRNAs were then extracted from the plasma and reverse transcribed. The expression levels of the seven microRNAs mentioned above were then measured in the reverse-transcribed samples using the TaqMen miRNA assay (Applied Biosystems). Table 2 below shows the characteristics of these 160 patients.

[0065] Table 2,

[0066] Subsequently, real-time PCR was performed on samples from the patient groups in Table 2 above to obtain the expression levels of the aforementioned seven microRNAs. Calculations on these expression levels further confirmed that certain miRNA ratios (ratios of expression levels of two RNAs, e.g., miR-22 / miR-142) differed between patients with and without coronary artery disease. These specific microRNA expression level ratios can be helpful in assessing the risk of coronary artery disease.

[0067] Example

[0068] Example 1

[0069] Prediction of coronary heart disease by blood biochemical substances

[0070] 1.1 The experimental groups were patients with coronary artery disease (CAD) or a control group without CAD (non-CAD). Blood triglycerides (TG) and high-density lipoprotein (HDL) were measured in both groups. As shown in Figure 2, the TG level was significantly higher in the CAD group than in the non-CAD group; as shown in Figure 3, the HDL level was significantly lower in the CAD group than in the non-CAD group; and as shown in Figure 4, the levels of both TG and HDL were significantly higher in the CAD group than in the non-CAD group.

[0071] 1.2 Next, receiver operating characteristic (ROC) curve analysis was performed on the blood biochemical substances triglycerides (TG) and high-density lipoprotein (HDL) in the non-CAD group and the CAD group to obtain the sensitivity and specificity for detecting coronary heart disease. Logistic regression analysis was then used to predict coronary heart disease patterns.

[0072] Table 3 Blood biochemical substances Area under the curve (AUC) p-value Triglycerides 0.8545 <0.0001 High-density lipoprotein 0.7663 <0.0001 Triglycerides + High-density lipoprotein 0.7715 <0.0001

[0073] The results are shown in Table 3 above. The area under the curve (AUC) for triglycerides reached 0.8545 (Figure 5), the AUC for high-density lipoprotein (HDL) reached 0.7663 (Figure 6), and the AUC for the combined triglycerides and HDL cholesterol reached 0.7715 (Figure 7). Therefore, triglycerides and HDL, whether alone or in combination, can serve as biomarkers for assessing coronary heart disease and provide accurate predictive results.

[0074] Comparative Example 1

[0075] The non-CAD group is the same as the CAD group in Example 1, except that the blood biochemical substances used are cholesterol and low-density lipoprotein. Cholesterol and low-density lipoprotein are both commonly used biochemical values ​​in the detection of coronary heart disease.

[0076] Table 4 Blood biochemical substances Area under the curve (AUC) p-value Low-density lipoprotein 0.5616 0.1452 cholesterol 0.5764 0.0254

[0077] The results are shown in Figures 8 to 10 and Table 4. There was no significant difference in low-density lipoprotein (LDL) levels between the CAD and non-CAD groups, and the area under the curve (AUC) was 0.5616. The AUC for cholesterol was 0.5764. Therefore, although triglycerides, high-density lipoprotein (HDL), LDL, and cholesterol are all commonly used blood biochemical substances for assessing coronary heart disease, the AUC values ​​for LDL and cholesterol are close to 0.5, and thus considered to have no discriminatory power.

[0078] Example 2

[0079] The ratio of expression levels of two microRNAs in predicting coronary heart disease

[0080] The non-CAD group is the same as the CAD group in Example 1, except that the ratio of the expression levels of the two microRNAs is analyzed using receiver operating characteristic curve analysis to obtain the sensitivity and specificity for detecting coronary heart disease. Then, logistic regression analysis is used for pattern prediction. Table 5 shows the ratio of the expression levels of the 10 microRNAs and the area under the curve.

[0081] Table 5 Group number ratio Area under the curve 1 = Ratio A (Equation 1) 0.7148 2 = Ratio B (2) 0.6989 3 = Ratio C (3) 0.6977 4 = Ratio D (4) 0.6800 5 = Ratio E (5) 0.6618 6 = Ratio F (6) 0.6628 7 = Ratio G (7) 0.6668 8 = Ratio H (Equation 8) 0.6538 9 = Ratio I Equation (9) 0.6095 10 = Ratio J (Equation 10) 0.5974

[0082] The results are shown in Figures 11 to 20. Figure 11 shows the scores (SC) of miR-484 / miR-222 calculated using logistic regression in samples from the control group (non-CAD) and patients with coronary artery disease (CAD). Figure 12 shows some embodiments of this disclosure, displaying the scores (SC) of miR-425-5p / miR-222 calculated using logistic regression in samples from the control group (non-CAD) and patients with coronary artery disease (CAD). Figure 13 shows some embodiments of this disclosure, displaying the scores (SC) of miR-425-5p / miR-222 calculated using logistic regression in samples from the control group (non-CAD) and patients with coronary artery disease (CAD). The following figures illustrate the results of logistic regression calculations (SC) for miR-22 / miR-142-5p in samples from patients with coronary artery disease (CAD); Figure 14 shows some embodiments of this disclosure, illustrating the results of logistic regression calculations (SC) for miR-484 / miR-425-5p in samples from control groups (non-CAD) and CAD patients; Figure 15 shows some embodiments of this disclosure, illustrating the results of logistic regression calculations for miR-146b / miR-484 in samples from control groups (non-CAD) and CAD patients. Figure 16 shows the results of the scoring (SC) of miR-146b / miR-22 calculated by logistic regression in some embodiments of this disclosure, in samples from the control group (non-CAD) and patients with coronary artery disease (CAD); Figure 17 shows the results of the scoring (SC) of miR-222 / miR-22 calculated by logistic regression in some embodiments of this disclosure, in samples from the control group (non-CAD) and patients with coronary artery disease (CAD); Figure 18 shows the results of the scoring (SC) of miR-146b / miR-22 in some embodiments of this disclosure, in samples from the control group (non-CAD) and patients with coronary artery disease (CAD); The figures show the results of logistic regression calculations (SC) for miR-21 / miR-146b in the CAD patient sample; Figure 19 shows the results of logistic regression calculations (SC) for miR-22 / miR-484 in the control (non-CAD) and CAD patient samples; Figure 20 shows the results of logistic regression calculations (SC) for miR-21 / miR-22 in the control (non-CAD) and CAD patient samples. After logistic regression analysis, Figures 12, 15, 16, 17, and 20 show that the CAD group scores were higher than the non-CAD group, while Figures 11, 13, 14, 18, and 19 show that the CAD group scores were lower than the non-CAD group. Therefore, the ratio of the 10 microRNA groups can all be used as biomarkers to assess whether coronary heart disease is present and to obtain accurate predictive results.

[0083] Comparative Example 2

[0084] The non-CAD group is the same as the CAD group in Example 2, and is also two of the seven microRNA expression levels in the preparation example. The difference is that the ratio of miR-142-5p / miR-484 is selected for receiver operating characteristic curve analysis to obtain the sensitivity and specificity of detecting coronary heart disease. Then, logistic regression analysis is used for pattern prediction.

[0085] Table Six, Group number ratio Area under the curve 1 = Ratio G (A) 0.502

[0086] The results are shown in Table 6. Although it is the ratio of two of the seven microRNA expression levels in the preparation example, it was surprisingly found that the area under the line value was close to 0.5, and was therefore considered to have no discriminative power. Therefore, it further highlights that the ratio of the ten microRNA expression levels in Example 2 as a biomarker for assessing whether coronary heart disease is present is unpredictable.

[0087] Example 3

[0088] The ratio of ten groups of microRNA expression levels and the prediction of one to four blood biochemical substances for coronary heart disease

[0089] The non-CAD group is the same as the CAD group in Example 1, except that the ratio of triglyceride and / or low-density lipoprotein expression levels in ten microRNA groups is used for pattern prediction by logistic regression analysis. The ratios of the ten microRNA expression levels are shown in Table 5 above. The model formula for detecting coronary heart disease is as follows.

[0090] Ratios of total cholesterol, triglycerides, LDL cholesterol, HDL cholesterol, and ten microRNA expression levels: Score = -3.191 + (1.550 * miR-484 / miR-222) - (1.769 * miR-425 / miR-22) + (0.681 * miR-22 / miR-142) + (0.748 * miR-484 / miR-425) + (2.573 * miR-146b / miR-484) – (0.459 * miR-146b / miR-22) + (1.329 * miR-222 / miR-22) + (0.211 * miR-21 / miR-146b) – (0.982 * miR-22 / miR-484) – (0.960 * miR-21 / miR-22) – (0.273 * TC) + (3.375 * TG) + (0.445 * HDL) + (0.706 * LDL) + (3.268 * TC / HDL) Equation (11); miR-484 / miR-222 is calculated using performance analysis. A performance value < 9.632 indicates that the patient has a risk of coronary heart disease, i.e., the converted score is 1 point; when the performance value ≥ 9.632, the score is 0 points; miR-425 / miR-22 is calculated using performance analysis. A performance value < 0.0215 indicates that the patient has a risk of coronary heart disease, i.e., the converted score is 1 point; when the performance value ≥ 0.0215, the score is 0 points; miR-22 / miR-142 is calculated using performance analysis. A performance value < A score of 3636 indicates a risk of coronary heart disease, with a converted score of 1. A score ≥ 3636 corresponds to a score of 0. For miR-484 / miR-425, a score < 9.341 indicates a risk of coronary heart disease, with a converted score of 1. A score ≥ 9.341 corresponds to a score of 0. For miR-146b / miR-484, a score > 0.033 indicates a risk of coronary heart disease, with a converted score of 1. A score ≤ 0.033 corresponds to a score of 0. For miR-146b / miR-22, a score > 0.004 indicates a risk of coronary heart disease, with a converted score of 1. A score ≤ 0.004 corresponds to a score of 0. For miR-222 / miR-22, a performance score > 0.012 indicates a risk of coronary heart disease, with a converted score of 1; a performance score ≤ 0.012 results in a score of 0. For miR-21 / miR-146b, a performance score < 1 indicates a risk of coronary heart disease.A score of 381 indicates a risk of coronary heart disease, with a converted score of 1 point; a score ≥ 1.381 corresponds to a score of 0 points. For miR-22 / miR-484, calculated using performance analysis, a performance value < 5.397 indicates a risk of coronary heart disease, with a converted score of 1 point; a performance value ≥ 5.397 corresponds to a score of 0 points. For miR-21 / miR-22, calculated using performance analysis, a performance value > 0.004 indicates a risk of coronary heart disease, with a converted score of 1 point; a performance value ≤ 0.004 corresponds to a score of 0 points. For TC, calculated using performance analysis, a performance value > 193.5 indicates a risk of coronary heart disease, with a converted score of 1 point; a performance value ≤ 193.5 corresponds to a score of 0 points. For TG, calculated using performance analysis, a performance value > 193.5 indicates a risk of coronary heart disease, with a converted score of 1 point; a performance value ≤ 193.5 corresponds to a score of 0 points. A score of 123.5 indicates a risk of coronary heart disease (CHD), with a converted score of 1. A score ≤ 123.5 corresponds to a score of 0. For HDL (High-Density LDL), calculated using performance analysis, a performance score < 45.5 indicates a risk of CHD, with a converted score of 1. A performance score ≥ 45.5 corresponds to a score of 0. For LDL (Large-Density LDL), calculated using performance analysis, a performance score > 126.5 indicates a risk of CHD, with a converted score of 1. A performance score ≤ 126.5 corresponds to a score of 0. For TC / HDL, calculated using performance analysis, a performance score > 3.85 indicates a risk of CHD, with a converted score of 1. A performance score ≤ 3.85 corresponds to a score of 0.

[0091] As shown in Figure 23, the CAD group score was significantly higher than the non-CAD group score (cutoff value: -0.0425), with an area under the curve of 0.9565, sensitivity of 88%, and specificity of 90%. Therefore, the ratio of total cholesterol, triglycerides, high-density lipoprotein, and / or low-density lipoprotein to the expression levels of ten microRNAs can be used as a biomarker for assessing coronary heart disease and obtaining accurate predictive results. Furthermore, as shown in Comparative Example 1, selecting total cholesterol or low-density lipoprotein alone cannot serve as a biomarker for assessing coronary heart disease. However, surprisingly, Figures 21 to 23 show that increasing total cholesterol and low-density lipoprotein as biomarkers not only does not reduce the area under the curve but actually increases it, thus further improving the accuracy of assessing coronary heart disease.

[0092] Example 4

[0093] Please refer to Figure 24, which illustrates an analyzer for assessing an individual's risk of developing coronary heart disease according to some embodiments disclosed herein. The analyzer 200 includes a detection device 210, a processing device 230, and a result output device 250.

[0094] The detection device 210 can detect the expression levels of a plurality of blood biochemical substances and a plurality of microRNAs (miRNAs) in a sample. The plurality of blood biochemical substances include total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein, wherein the content of blood biochemical substances is calculated as described in the preparation example. The microRNAs include miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof, wherein the expression levels are calculated as described in the preparation example.

[0095] The computing device 230 can perform calculations on the expression of micro ribonucleic acid, and the calculation methods include the following.

[0096] (1) As in Example 1 (1.1), compare the contents of at least one or more blood biochemical substances: triglycerides and / or high-density lipoprotein in the test sample and the control group to obtain the calculation results.

[0097] (2) As in Example 1 (1.2), the content of at least one or more blood biochemical substances of the test specimen: triglycerides and / or high-density lipoprotein, is substituted into the model formula obtained by receiver operating characteristic curve analysis in Example 1 (1.2) to obtain the calculation result. When the score value of the test specimen is greater than the score value of the non-CAD group, it is assessed as having a risk of coronary heart disease.

[0098] (3) As in Example 2, the ratio of the expression levels of the two microRNAs of the test specimen is substituted into the model formula obtained by receiver operating characteristic curve analysis in Example 2 to obtain the calculation result. When the score of the test specimen is greater than the score of the non-CAD group, it is assessed as having a risk of coronary heart disease.

[0099] (4) As in Example 3, the ratio of triglyceride and / or low-density lipoprotein content of the test specimen to the expression levels of ten groups of microRNAs is substituted into the model formula obtained by receiver operating characteristic curve analysis in Example 4 to obtain the calculation result. When the test specimen score is greater than the non-CAD group score as shown in Figures 12, 15, 16, 17, and 20, it is assessed as having a risk of coronary heart disease. When the test specimen score is less than the non-CAD group score as shown in Figures 11, 13, 14, 18, and 19, it is assessed as having a risk of coronary heart disease.

[0100] Therefore, this disclosure provides a kit and method for detecting coronary artery disease using triglycerides and / or high-density lipoprotein, or total cholesterol, triglycerides, high-density lipoprotein and / or low-density lipoprotein combined with miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, and miR-484. Subsequently, for patients suspected of having coronary artery disease, the expression levels of these microRNAs can be detected, or they can be used to track patients who have received treatment to assess the effectiveness of treatment.

[0101] In some embodiments, there is an application of a blood biochemical substance in the preparation of a reagent for predicting coronary heart disease, characterized in that the blood biochemical substance comprises triglycerides, high-density lipoprotein, or a combination thereof.

[0102] In some embodiments, an application of a composition in the preparation of a reagent for predicting coronary heart disease is provided, characterized in that the composition comprises blood biochemicals and microRNAs, the blood biochemicals comprising total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, or combinations thereof, and the microRNAs comprising miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof.

[0103] Although the present disclosure has been disclosed above with reference to embodiments, it is not intended to limit the present disclosure. Anyone skilled in the art can make various modifications and refinements without departing from the spirit and scope of the present disclosure. Therefore, the scope of protection of the present disclosure shall be determined by the claims attached. [Simplified Explanation of the Diagram]

[0104] The various aspects of this disclosure will be most readily understood when read in conjunction with the accompanying drawings. It should be noted that, according to industry standard operating procedures, the various feature structures may not be drawn to scale. In fact, for clarity of explanation, the dimensions of the various feature structures can be arbitrarily increased or decreased. To make the above and other objects, features, advantages, and embodiments of this disclosure more apparent and understandable, the accompanying drawings are described below: Figure 1 illustrates a process for screening blood biochemical substances and microRNAs for detecting coronary artery disease according to some embodiments of this disclosure. Figure 2 shows the TG content in samples from a control group (non-CAD) and coronary artery disease patients (CAD) according to some embodiments of this disclosure. Figure 3 shows the HDL content in samples from a control group (non-CAD) and coronary artery disease patients (CAD) according to some embodiments of this disclosure. Figure 4 shows the TG and HDL content in samples from a control group (non-CAD) and coronary artery disease patients (CAD) according to some embodiments of this disclosure. Figure 5 shows the results of receiver operating characteristic (ROC) curve analysis of TG levels in samples from control groups (non-CAD) and coronary artery disease (CAD) patients, according to some embodiments of this disclosure. Figure 6 shows the results of receiver operating characteristic (ROC) curve analysis of HDL levels in samples from control groups (non-CAD) and coronary artery disease (CAD) patients, according to some embodiments of this disclosure. Figure 7 shows the results of receiver operating characteristic (ROC) curve analysis of TG and HDL levels in samples from control groups (non-CAD) and coronary artery disease (CAD) patients, according to some embodiments of this disclosure. Figure 8 shows the results of LDL levels in samples from control groups (non-CAD) and coronary artery disease (CAD) patients, according to some embodiments of this disclosure. Figure 9 shows the results of receiver operating characteristic (ROC) curve analysis of LDL levels in samples from control groups (CAD) and coronary artery disease (CAD) patients, according to some embodiments of this disclosure. Figure 10 shows some embodiments of this disclosure, displaying the results of TC analysis using receiver operating characteristic (ROC) curves in samples from the control group (Ctrl) and coronary artery disease patients (CAD).Figures 11 through 20 illustrate some embodiments of this disclosure, showing the results of signature scores (SCs) calculated using logistic regression in samples from a control group (non-CAD) and patients with coronary artery disease (CAD). Figure 11 shows miR-484 / miR-222, Figure 12 shows miR-425 / miR-22, Figure 13 shows miR-22 / miR-142, Figure 14 shows miR-484 / miR-425, Figure 15 shows miR-146b / miR-484, Figure 16 shows miR-146b / miR-22, Figure 17 shows miR-222 / miR-22, Figure 18 shows miR-21 / miR-146b, Figure 19 shows miR-22 / miR-484, and Figure 20 shows miR-21 / miR-22. Figure 21 illustrates some embodiments of this disclosure, showing the results of receiver operating characteristic (ROC) curve analysis of ten microRNA expression levels and four clinical biochemical values ​​in samples from control groups (non-CAD) and CAD patients. Figure 22 illustrates some embodiments of this disclosure, showing the sensitivity and specificity results of the score (SC) calculated by logistic regression of ten microRNA expression levels and four clinical biochemical values ​​in samples from control groups (non-CAD) and CAD patients. Figure 23 illustrates some embodiments of this disclosure, showing the results of the score (SC) calculated by logistic regression of ten microRNA expression levels and four clinical biochemical values ​​in samples from control groups (non-CAD) and CAD patients. Figure 24 is a schematic diagram of an analyzer for assessing an individual's risk of developing coronary artery disease, based on some embodiments of this disclosure.

Claims

1. A method for assessing an individual's risk of developing coronary heart disease, comprising: measuring the levels of a blood biochemical substance in a sample of the individual, the blood biochemical substance including triglycerides, high-density lipoprotein, or a combination thereof; and comparing the levels of the same blood biochemical substance in a negative control group with those in the sample, wherein, When the triglyceride level in the sample is higher than that in the negative control group, and / or when the high-density lipoprotein level in the sample is lower than that in the negative control group, the individual is assessed as having a risk of developing coronary heart disease.

2. The method as described in claim 1, wherein the negative control group is taken from a group of individuals known not to have coronary heart disease.

3. The method as described in claim 1, wherein the sample is blood, ascites, urine, feces, or saliva.

4. A method for assessing an individual's risk of coronary heart disease, comprising: measuring the levels of blood biochemicals in a sample of the individual, the blood biochemicals including total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, or combinations thereof; measuring the expression levels of at least two microRNAs in the sample of the individual, the at least two microRNAs including miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof; and generating at least one first ratio, the at least one first ratio being generated from the expression levels of the at least two microRNAs in the sample using the following formula: = First ratio A Equation (1); = First ratio B Equation (2); = First ratio C Equation (3); = First ratio D Equation (4); = First ratio E Equation (5); = First ratio F Equation (6); = First ratio G Equation (7); = First ratio H Equation (8); = First ratio I Equation (9); or = First ratio J Equation (10); A positive control group and a negative control group are compared using at least one second ratio obtained from the same blood biochemical substance and the same at least two microRNAs as the sample. A model formula is generated using logistic regression, and the negative control group generates a negative control score. The at least one second ratio is generated by the following formula: = Second ratio A Equation (1-1); = Second ratio B Equation (2-1); = Second ratio C Equation (3-1); = Second ratio D Equation (4-1); = Second ratio E Equation (5-1); = Second ratio F Equation (6-1); = Second ratio G Equation (7-1); = Second ratio H Equation (8-1); = Second ratio I Equation (9-1); or = Second ratio J Equation (10-1); and The sample score is generated by applying the model formula to the content of the blood biochemical substance in the sample of the individual and the at least one first ratio. When the sample score of the first ratio B, the first ratio E, the first ratio F, the first ratio G, the first ratio J or the combination thereof is greater than the negative control score of the second ratio B, the second ratio E, the second ratio F, the second ratio G, the second ratio J or the combination thereof, the individual is assessed as having a risk of coronary heart disease; or when the sample score of the first ratio A, the first ratio C, the first ratio D, the first ratio H, the first ratio I or the combination thereof is less than the negative control score of the second ratio A, the second ratio C, the second ratio D, the second ratio H, the second ratio I or the combination thereof, the individual is assessed as having a risk of coronary heart disease.

5. The method as described in claim 4, wherein the positive control group is taken from a group of individuals known to have coronary heart disease, and the negative control group is taken from a group of individuals known not to have coronary heart disease.

6. The method as described in claim 4, wherein the sample is blood, ascites, urine, feces, or saliva.

7. The method as described in claim 4, wherein the at least two microRNAs are seven microRNAs: miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, and miR-484, wherein the at least one first ratio is ten first ratios: the first ratio A, the first ratio B, the first ratio C, the first ratio D, the first ratio E, the first ratio F, the first ratio G, the first ratio H, the first ratio I, and the first ratio J, wherein the at least one second ratio is ten second ratios: the second ratio A, the second ratio B, the second ratio C, the second ratio D, the second ratio E, the second ratio F, the second ratio G, the second ratio H, the second ratio I, and the second ratio J.

8. The method as described in claim 7, wherein the miR-21 includes miR-21-5p, the miR-22 includes miR-22-3p, the miR-142 includes miR-142-5p, the miR-146b includes miR-146b-5p, the miR-222 includes miR-222-3p, or the miR-425 includes miR-425-5p.

9. The method as described in claim 7, wherein the model formula is: Score = -3.191 + (1.550 * miR-484 / miR-222) - (1.769 * miR-425 / miR-22) + (0.681 * miR-22 / miR-142) + (0.748 * miR-484 / miR-425) + (2.573 * miR-146b / miR-484) - (0.459 * miR-146b / miR-22) + (1.329 * miR-222 / miR-22) + (0.211 * miR-21 / miR-146b) - (0.982 * miR-22 / miR-484) - (0.960 * miR-21 / miR-22) - (0.273 * Total cholesterol) + (3.375 * triglycerides) + (0.445 * high-density lipoprotein) + (0.706 * low-density lipoprotein) + (3.268 * total cholesterol / high-density lipoprotein) Equation (11).

10. A kit for assessing whether an individual has coronary heart disease, comprising: at least one first reagent for identifying the level of at least one blood biochemical substance in a sample of the individual, the at least one blood biochemical substance comprising triglycerides, high-density lipoprotein, or a combination thereof.

11. The kit as claimed in claim 10 further comprises at least one second reagent for identifying the expression level of microRNA in the sample of the individual, wherein the microRNA comprises miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or a combination thereof.

12. The kit as claimed in claim 11, wherein the at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-484 and miR-222; wherein the at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-425 and miR-22; wherein the at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-22 and miR-142; wherein the at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-484 and miR-425; wherein the at least one second reagent comprises a plurality of second reagents for identifying microRNAs in the sample as miR-146b and miR-484; The at least one second reagent comprises multiple second reagents, which are used to identify the microRNAs in the sample as miR-146b and miR-22; the at least one second reagent comprises multiple second reagents, which are used to identify the microRNAs in the sample as miR-222 and miR-22; the at least one second reagent comprises multiple second reagents, which are used to identify the microRNAs in the sample as miR-21 and miR-146b; the at least one second reagent comprises multiple second reagents, which are used to identify the microRNAs in the sample as miR-22 and miR-484; or the at least one second reagent comprises multiple second reagents, which are used to identify the microRNAs in the sample as miR-21 and miR-22.

13. The kit as claimed in claim 12, wherein miR-21 comprises miR-21-5p, miR-22 comprises miR-22-3p, miR-142 comprises miR-142-5p, miR-146b comprises miR-146b-5p, miR-222 comprises miR-222-3p, or miR-425 comprises miR-425-5p, wherein the at least one blood biochemical further comprises total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, or a combination thereof.

14. The kit as claimed in claim 11, wherein the at least one second reagent comprises a primer pair, a probe, or a combination thereof, wherein the at least one first reagent comprises a primer pair, a probe, or a combination thereof.

15. An analyzer for assessing an individual's risk of coronary heart disease, comprising: a detection device configured to detect the level of at least one blood biochemical substance in a sample of the individual, the blood biochemical substance including triglycerides, high-density lipoprotein, or a combination thereof; a calculation device configured to perform calculations on the levels of the blood biochemical substance, including comparing the levels of the same blood biochemical substance in a negative control group with those in the sample, and obtaining a calculation result; and a result output device configured to output the calculation result, wherein... When the triglyceride level in the sample is higher than that in the negative control group, and / or when the high-density lipoprotein level in the sample is lower than that in the negative control group, the individual is assessed as having a risk of developing coronary heart disease.

16. The analyzer as claimed in claim 15, wherein the detection device is further configured to detect the expression levels of at least two microRNAs in the sample of the individual, the at least two microRNAs comprising miR-21, miR-22, miR-142, miR-146b, miR-222, miR-425, miR-484, or combinations thereof, wherein the computing device further generates at least one first ratio, the at least one first ratio being generated from the expression levels of the at least two microRNAs in the sample using the following formula: = First ratio A Equation (1); = First ratio B Equation (2); = First ratio C Equation (3); = First ratio D Equation (4); = First ratio E Equation (5); = First ratio F Equation (6); = First ratio G Equation (7); = First ratio H Equation (8); = First ratio I Equation (9); or = First ratio J Equation (10); A positive control group and a negative control group are compared using at least one second ratio obtained from the same blood biochemical substance and the same at least two microRNAs. A model formula is generated using logistic regression, and the negative control group generates a negative control score. The at least one second ratio is generated by the following formula: = Second ratio A Equation (1-1); = Second ratio B Equation (2-1); = Second ratio C Equation (3-1); = Second ratio D Equation (4-1); = Second ratio E Equation (5-1); = Second ratio F Equation (6-1); = Second ratio G Equation (7-1); = Second ratio H Equation (8-1); = Second ratio I Equation (9-1); or = Second ratio J Equation (10-1); as well as The sample score is generated by applying the model formula to the content of the blood biochemical substance in the sample of the individual and the at least one first ratio; the sample score and the negative control score are the result of the calculation. The determination result output device is used to output the calculation result. When the sample score value of the first ratio B, the first ratio E, the first ratio F, the first ratio G, the first ratio J or the combination thereof is greater than the negative control score value of the second ratio B, the second ratio E, the second ratio F, the second ratio G, the second ratio J or the combination thereof, the individual is assessed as having a risk of coronary heart disease; or when the sample score value of the first ratio A, the first ratio C, the first ratio D, the first ratio H, the first ratio I or the combination thereof is less than the negative control score value of the second ratio A, the second ratio C, the second ratio D, the second ratio H, the second ratio I or the combination thereof, the individual is assessed as having a risk of coronary heart disease.

17. The analyzer as claimed in claim 16, wherein the detection device is further configured to detect the individual's total cholesterol, triglycerides, low-density lipoprotein, and high-density lipoprotein.

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