CN117969836A - Aortic valve calcification stenosis diagnosis model constructed based on plasma inflammatory proteins and application - Google Patents
Aortic valve calcification stenosis diagnosis model constructed based on plasma inflammatory proteins and application Download PDFInfo
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Abstract
The invention discloses an aortic valve calcified stenosis diagnosis model constructed based on plasma inflammatory proteins and application thereof. The invention provides the use of a substance for detecting the content of inflammatory proteins MMP1 (human matrix metalloproteinase 1) and SIRT2 (human deacetylase 2) for the preparation of a product having at least one of the following functions: 1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis; 2) Screening or assisting in screening aortic valve calcification stenosis; 3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve; 4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve. The inflammatory protein diagnosis model constructed by the invention can diagnose, assist diagnosis, forecast or monitor the aortic valve calcification stenosis, assist doctors to make clinical decisions in time and save medical resources.
Description
Technical Field
The invention belongs to the technical field of biological information, and particularly relates to an aortic valve calcification stenosis diagnosis model constructed based on plasma inflammatory proteins and application thereof.
Background
Valvular heart disease is a common heart disease in China, the prevalence rate of people over 70 years old can reach 30% along with aging of population, calcified aortic valve stenosis is one of the most common heart valve diseases, and when mild valvular stenosis occurs, the development of the disease is unavoidable. While patients who develop severe aortic stenosis, 50% of the patients will experience heart failure or cardiac death after 3 years without intervention. Therefore, how to accurately, timely and early diagnose aortic valve calcification stenosis is an important and urgent task for doctors, especially primary doctors.
Echocardiography is the first choice for diagnosing aortic valve calcification stenosis, and the technology mainly evaluates the aortic valve stenosis degree and whether calcification exists or not through hemodynamic indexes such as average valve crossing pressure difference, maximum aortic valve crossing flow rate and the like and echo indexes. However, for patients with partially degenerative aortic valve calcified stenosis, the patients have hidden diseases and slow progress, the defects of valve stenosis and/or insufficiency are caused, the effects of blood flow dynamics are small, the specific clinical manifestations are often lacking, anatomical structures such as valve thickening and calcification are difficult to observe in ultrasound, missed diagnosis and misdiagnosis are easy to occur, and a doctor with abundant experience is required to conduct ultrasound diagnosis. The heart CT diagnosis valve calcification is more sensitive than the ultrasonic cardiography, and the calcification can be positioned and quantitatively analyzed, but the examination cost is higher, and the social and national medical cost burden is greatly increased, so that a simple and rapid mode is needed to assist in diagnosing the aortic valve calcified stenosis.
There is increasing evidence that valve inflammation is an important initiating event that promotes the progression of valve calcification stenosis. As calcified stenosis progresses, immune cells of the valve secrete a series of inflammatory proteins, such as cytokines, chemokines, etc., forming an immune microenvironment that promotes phenotypic transformation of valve stromal cells, resulting in calcium deposition and mineralization of valve tissue. Therefore, the detection of the circulating inflammatory protein group and the determination of the specific inflammatory protein combination are helpful for early diagnosis of aortic valve calcification stenosis. It is proposed that the combination of circulating inflammatory proteins can be used as a novel diagnostic model to assist in the diagnosis of aortic valve calcification stenosis.
Disclosure of Invention
The invention aims to provide a diagnosis model for aortic valve calcification stenosis based on plasma inflammatory proteins and application thereof.
In a first aspect, the invention provides the use of a substance for detecting the content of inflammatory proteins MMP1 (human matrix metalloproteinase 1) and SIRT2 (human deacetylase 2) for the preparation of a product having at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve.
The subject for the above application is a patient suspected of aortic valve calcified stenosis, in particular a patient clinically diagnosed with a suspected aortic valve calcified stenosis.
One of the purposes of the above screening or auxiliary screening of aortic valve calcified stenosis is to screen patients in need of further cardiac ultrasound or cardiac CT examination, which helps the primary doctor to diagnose aortic valve stenosis calcification accurately, timely and early.
In a second aspect, the invention provides the use of a substance that detects the levels of inflammatory proteins MMP1 and SIRT2 and a mediator that is loaded with the following inflammatory protein scoring model formula or its visual nomogram, in the preparation of a product having at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from aortic valve calcification stenosis;
the inflammatory protein scoring model formula is shown in the following formula I:
Model score = (Formula I);
Wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant.
The visual alignment of the inflammatory protein scoring model formula described above is shown in particular in fig. 12.
In the above, the substances for detecting the content of inflammatory proteins MMP1 and SIRT2 are substances for detecting the content of inflammatory proteins MMP1 and substances for detecting the content of inflammatory proteins SIRT 2; each substance includes a probe or antibody that binds to MMP1 protein or mRNA thereof, and the like. In an embodiment of the invention, the substance detecting the content of inflammatory protein MMP1 is a human matrix metalloproteinase 1 (MMP-1) ELISA kit, the substance detecting the content of inflammatory protein SIRT2 is a human deacetylase 2 (SIRT 2) ELISA kit, and each kit comprises an antibody binding to a corresponding protein.
The detection of the content of inflammatory proteins MMP1 and SIRT2 is to detect the content of inflammatory proteins MMP1 and SIRT2 in the blood plasma of a subject.
The medium refers to a carrier for storing data, and may be a magnetic tape, a magnetic disk, a floppy disk, an optical disk, a magneto-optical disk, ROM, PROM, VCD, DVD, a hard disk, a flash Memory, a usb disk, a CF card, an SD card, an MMC card, an SM card, a Memory Stick (Memory Stick), an xD card, or the like.
In a third aspect, the invention provides a kit comprising a substance for detecting the levels of inflammatory proteins MMP1 and SIRT2 as described in the first or second aspects.
The kit further comprises a medium loaded with the following inflammatory protein scoring model formula or a visual nomogram thereof;
the inflammatory protein scoring model formula is shown in the following formula I:
Model score = (Formula I);
wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant.
The kit described above has at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve.
In a fourth aspect, the present invention provides an apparatus for diagnosing or aiding in diagnosing aortic valve calcification stenosis, comprising a data receiving module, a data processing module and a data output module;
The data receiving module is used for receiving the content of MMP1 and SIRT2 proteins in the blood plasma of a subject;
The data processing module is used for substituting the content of MMP1 and SIRT2 proteins into an inflammatory protein scoring model to obtain the inflammatory protein scoring value of the subject,
The inflammatory protein scoring model is the following formula or a visual alignment chart thereof;
the formula of the inflammatory protein scoring model is shown in the following formula I:
Model score = (Formula I);
wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant;
The data output module is used for outputting whether the subject suffers from the aortic valve calcification stenosis or the aortic valve calcification stenosis degree result according to the inflammatory protein score value.
Outputting whether the subject suffers from the aortic valve calcification stenosis or the aortic valve calcification stenosis degree result according to the inflammatory protein score value, specifically calculating the cut-off value of the inflammatory protein combination score, and diagnosing the aortic valve calcification stenosis when the score is more than or equal to 0.4911. Sensitivity in training set is 90.0% and specificity is 90.0%; sensitivity in the validation set was 78.0% and specificity was 86.0%.
The inflammatory protein combination score of the invention has an area AUC under the curve of a training set of 0.907 (P < 0.001), and has an area AUC under the curve of a verification set of 0.892 (P < 0.001), thereby having better prediction effect on aortic valve calcification stenosis.
In a fifth aspect, the present invention provides a computer apparatus comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to effect the steps of:
S1) data receiving: receiving the levels of MMP1 and SIRT2 proteins in the plasma of the subject;
S2) data processing: substituting the content of MMP1 and SIRT2 proteins into an inflammatory protein scoring model to obtain an inflammatory protein scoring value of a subject;
the inflammatory protein scoring model is the following formula or a visual alignment chart thereof;
the formula of the inflammatory protein scoring model is shown in the following formula I:
Model score = (Formula I);
wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
x (MMP 1) is the content of MMP1, X (SIRT 2) is the content of SIRT2, and e is a natural constant;
s3) data output: outputting whether the subject suffers from the aortic valve calcification stenosis or the aortic valve calcification stenosis degree result according to the inflammatory protein score value.
In a sixth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the fifth aspect.
In a seventh aspect, the invention provides a computer program product comprising a computer program which, when executed by a processor, carries out the steps of the fifth aspect.
In an eighth aspect, the present invention provides the use of a substance for detecting the content of inflammatory proteins MMP1 and SIRT2 as described in the first aspect, a kit of the third aspect or a device of the fourth aspect or a computer device of the fifth aspect or a computer program product of the seventh aspect or a product of the first aspect for the preparation of a product having at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve.
In the above, the product is a kit.
The invention provides a model for noninvasively diagnosing aortic valve calcification stenosis based on inflammatory proteins. The inflammatory protein diagnosis model constructed by the invention can diagnose, assist diagnosis, forecast or monitor the aortic valve calcification stenosis, assist doctors to make clinical decisions in time and save medical resources. In addition, the inventor builds a visual scoring table based on the scoring model, so that the result of the prediction model is more readable, and the extensive screening of basic doctors and the interpretation of the scoring result of patients are facilitated. In conclusion, the above results support that the prediction model based on plasma inflammatory protein metabolites is helpful for clinically diagnosing aortic valve calcification stenosis, screening patients needing further cardiac ultrasound or cardiac CT examination, and helping the primary doctor to accurately, timely and early diagnose aortic valve stenosis calcification.
Compared with the prior art, the invention has the following advantages:
1. Providing a non-invasive diagnostic model;
2. All patients suspected of aortic valve calcification stenosis are achievable;
3. help assess aortic valve stenosis;
4. Auxiliary screening is carried out on patients needing cardiac ultrasound and cardiac CT detection, so that excessive medical treatment is avoided;
5. convenient operation and rapid diagnosis.
Drawings
FIG. 1 is a flow chart of the screening and construction of a model for characterizing plasma inflammatory proteins and scoring according to the present invention.
FIG. 2 is a volcanic plot of differential inflammatory proteins in aortic valve calcified stenosis patients versus control patients.
FIG. 3 is a forest map of independent predicted inflammatory proteins screened by a multifactor Logistic regression analysis.
FIG. 4 is a scatter plot of inflammatory proteins that are correlated to the extent of aortic stenosis.
FIG. 5 is a bar graph of quantitative expression levels of inflammatory proteins (MMP 1 and SIRT 2) in a training set.
FIG. 6 is a graph showing the parameters related to constructing inflammatory protein combinatorial Logistic regression scores.
Fig. 7 is a plot of inflammation model scores in a training set.
Fig. 8 is a ROC curve of inflammatory protein scoring model in training set.
Fig. 9 is a dot pattern of scores for the validated set of inflammation models.
Fig. 10 is an evaluation of efficacy of diagnosing aortic valve calcification stenosis on plasma inflammatory proteins and scoring models.
FIG. 11 is a scatter plot of the correlation of inflammatory protein scores with aortic stenosis.
Fig. 12 is a visual alignment of inflammatory protein scoring models.
Detailed Description
The experimental methods used in the following examples are conventional methods unless otherwise specified.
Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
An overall flow chart of the screening and construction of plasma inflammatory protein scores of the present invention is shown in figure 1.
Example 1 determination of plasma inflammatory proteins associated with aortic valve calcification stenosis
1. Plasma inflammatory protein level determination
1. Medical record selection and sample preparation
1.1, Inclusion criteria: 1) Age is more than or equal to 18 years old; 2) Heart ultrasound determines that the aortic valve is stenosed, and the maximum aortic valve crossing speed is greater than 250cm/s; 3) During aortic valve replacement surgery, pathological staining determines calcification of the valve;
1.2, exclusion criteria: 1) The baseline information is imperfect; 2) Aortic valve regurgitation; 3) No heart ultrasound results or poor image quality; 100 cases of aortic valve calcified stenosis groups were finally collected.
1.3, Control samples: the control subjects were diagnosed by cardiac ultrasound without aortic valve calcification stenosis, and 100 healthy control groups were finally collected.
1.4, Sample preparation:
collecting peripheral blood sample with EDTA-containing vacuum tube, shaking vacuum tube, standing, centrifuging (3000 rpm,10 min), rapidly sucking supernatant with disposable dropper, packaging, and preserving at-80deg.C to obtain plasma sample.
2. Plasma inflammatory protein level determination
2.1, Carrying out protein detection from a 5ul blood plasma sample, and diluting the sample into a proper concentration range according to experimental requirements so as to ensure the sensitivity and accuracy of the experiment.
2.2, According to the experimental requirement, select Olink inflammatory protein experimental plate (Olink Target 96 Inflammation panel), take out the experimental plate from the refrigerator, and restore to room temperature.
2.3, Adding the sample into the experimental board, placing the experimental board into temperature control equipment, performing antibody binding reaction, and using the protein in the sample to bind with the oligonucleotide sequence antibody carried and designed on the Olink experimental board.
2.4, Washing and elution: the assay plate was removed and subjected to an elution step to remove unbound material and retain bound material. The eluted material is subjected to DNA amplification reaction, and the bound material generates corresponding DNA product.
2.5, Carrying out DNA amplification reaction on the eluted substances to enable the combined substances to generate corresponding DNA products. The DNA product is purified to remove impurities and byproducts. And quantifying the purified DNA by using a fluorescence quantitative instrument, and ensuring the concentration and quality of the sample.
2.6, Placing the DNA sample into Olink analyzer, reading fluorescent signal of sample, making numerical conversion and attribution treatment by NPX Signature software software so as to obtain the relative quantitative data of 92 inflammatory proteins.
2. Preliminary screening of inflammatory proteins associated with aortic valve calcified stenosis
From the 100 healthy control groups and the 100 aortic valve calcified stenosis groups, 28 valvular calcified stenosis groups were randomly selected according to age-sex matching, and 28 age-sex matching healthy control groups. The relative amounts of 92 circulating inflammatory proteins were compared to the healthy control group (28) and valve calcification stenosis group (28) and the two groups were screened for differential proteins using the Wilcoxon test nonparametric test, all statistical tests were double tailed and FDR values <0.01 were considered as significantly different inflammatory proteins.
The results of the differential analysis are shown in the volcanic chart of fig. 2, with the ordinate being-log 10 (FDR), the abscissa being fold of change, 8 differential inflammatory proteins in total, STAM binding protein (STAMBP), acetylase 2 (SIRT 2), eukaryotic translation initiation factor 4E binding protein 1 (4E-BP 1), human matrix metalloproteinase-1 (MMP-1), human sulfotransferase 1A1 (ST 1 A1), monocyte chemotactic protein (MCP 4), adenosine deaminase protein (ADA) and interleukin 8 (IL 8), respectively, and the relative levels of the 8 circulating inflammatory proteins are elevated in the aortic valve calcified stenosis group.
3. Screening and independent prediction of inflammatory proteins in aortic valve calcification stenosis
Based on screening 8 different circulating inflammatory proteins, a method for Logistic regression screening for independent prediction of inflammatory proteins may include: by correcting conventional clinical risk factors for multifactorial regression, the clinical covariates used for correction can be gender and age, all statistical tests are two-tailed, p-values <0.05 are considered as independently related inflammatory proteins, ratio (OR) >1 is considered as positively correlated with the occurrence of valvular calcification stenosis, OR <1 is considered as negatively correlated with the occurrence of valvular calcification stenosis.
The Logistic results are shown in the forest chart of fig. 3, 7 inflammatory proteins are independently associated with the occurrence of aortic valve calcification stenosis, STAM binding protein (STAMBP), acetylase 2 (SIRT 2), eukaryotic translation initiation factor 4E binding protein 1 (4E-BP 1), human matrix metalloproteinase-1 (MMP-1), human sulfotransferase 1A1 (ST 1 A1), monocyte chemotactic protein (MCP 4) and adenosine deaminase protein (ADA), respectively, and these 7 inflammatory proteins are positively associated with the occurrence of aortic valve calcification stenosis.
4. Determining inflammatory proteins associated with the extent of aortic valve calcification stenosis
The three identified independent predicted inflammatory proteins were analyzed for their correlation with aortic stenosis (maximum aortic cross-valve flow rate) in patients with aortic calcified stenosis using a Spearman bivariate correlation analysis, all statistical tests were two-tailed, with P values <0.05 considered inflammatory proteins significantly correlated with the phenotype of valve stenosis.
The Spearman results are shown in the correlation scatter diagram of fig. 4, the ordinate shows the content of each inflammatory protein, the abscissa shows the maximum aortic valve crossing flow rate, two inflammatory protein matrixes, namely human metalloproteinase-1 (MMP 1) and human acetylase-2 (SIRT 2), are related to the maximum aortic valve crossing flow rate, the correlation coefficient is R (MMP1)=0.852（p<0.01）;R(SIRT2) = 0.483 (p < 0.01), the result shows that the two inflammatory proteins, namely MMP1 (Gene ID: 4312) and SIRT2 (Gene ID: 22933), are positively correlated with the calcified stenosis degree of the aortic valve, and the higher the content of the two inflammatory proteins is, the more the stenosis degree of a patient is.
Example 2 construction of inflammatory protein scoring model by inflammatory protein (MMP 1 and SIRT 2) combination
1. Preparation of inflammatory protein scoring model
1. Detection of inflammatory protein (MMP 1 and SIRT 2) content
The healthy control group (50 cases) and the valvular calcification stenosis group (50 cases) are used as training sets, and the content of MMP1 and SIRT2 in blood plasma of each sample in the training sets is detected. Detection was performed using a human matrix metalloproteinase 1 (MMP-1) ELISA kit (Vankavid, F0071-A) and a human deacetylase 2 (SIRT 2) ELISA kit (Vankavid, F0119-HA), which contained a detection plate, a standard, a diluent, a horseradish peroxidase-labeled detection antibody, a wash solution, a substrate solution, and a stop solution. The method comprises the following steps: the kit was first rewarmed at room temperature for 20 minutes and the assay plate removed. The assay plate contained standard wells and sample wells, and 50 μl of standard of different concentrations was added to the standard wells. 10 mu L of the detection sample and 40 mu L of the sample diluent are added into the sample hole. And adding 100 mu L of horseradish peroxidase-labeled detection antibody into each of the standard sample hole and the sample hole, sealing the reaction hole by using a sealing plate film, and keeping the temperature at 37 ℃ for 60 minutes. Then the liquid is discarded, each hole is filled with the washing liquid, and the washing liquid is thrown away after standing for 1 minute. Finally, 50. Mu.L of substrate solution was added to each well, incubated at 37℃for 15 minutes in the absence of light, 50. Mu.L of stop solution was added, and the absorbance of each well was measured at a wavelength of 450nm using an enzyme-labeled instrument. And (3) taking the concentration of the standard substance as an abscissa and the corresponding absorbance value as an ordinate, drawing a linear regression curve of the standard substance, and calculating the concentration value of each sample according to a curve equation. Using the T-test, plasma was analyzed for differences in MMP1 and SIRT2 levels between the two groups, with P values <0.05 being considered to be differences between the two groups.
The detection results are shown in the dot-pattern of the inflammatory protein expression level in FIG. 5, and the ordinate represents the MMP1 or SIRT2 content (marked as the level in the figure). The average MMP1 level of the healthy control group is 142.84 ng/ml, the average MMP1 level of the valvular calcification stenosis group is 200.17ng/ml, and the MMP1 level of the valvular calcification stenosis group is significantly higher than that of the healthy control group (P value < 0.01). The average SIRT2 value of the healthy control group is 15.99 ng/ml, the average SIRT2 value of the valvular calcification stenosis group is 20.71ng/ml, and the SIRT2 value of the valvular calcification stenosis group is significantly higher than that of the healthy control group (P value < 0.01).
2. Construction of inflammatory protein scoring model by logistic regression
Based on the inflammatory protein (MMP 1 and SIRT 2) content of the training set, a logistic regression was used to construct an inflammatory protein scoring model. The result is shown in FIG. 6, the regression coefficients of the two proteins are respectively beta (MMP1) of 0.035, beta (SIRT2) of 0.283 and constant of-11.206, and the scoring equation of the model is that
Logit(P)= -11.206+0.035*X(MMP1)+ 0.283*X(SIRT2)
Model score =(Formula I);
In the above formula, X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant.
Model scores were obtained for the healthy control group (50 cases) and the valvular calcified stenosis group (50 cases), and were analyzed for differences between the two groups using T-test, and a P value <0.05 was considered to be different between the two groups. Specific results as shown in the dot plot of the model score of fig. 7 (labeled as inflammatory protein score in the figure) show that in the training set, the mean of the model score of the healthy control group is 0.2196, the mean of the model score of the valvular calcified stenosis group is 0.7869, and the inflammatory score of the valvular calcified stenosis group is significantly higher than that of the healthy control group (P < 0.01).
The model scores of the two groups of subjects were plotted using SPSS 22.0 software on an ROC curve to obtain a series of sensitivity and 1-specificity values. The about sign index is obtained by using the sensitivity- (1-specificity), and the about sign indexes are ordered to obtain the maximum value of the about sign index, namely the optimal critical point (cut-off). The optimal critical point (cut-off) of the model score is 0.4911, when the model score is greater than or equal to 0.4911, the aortic valve calcified stenosis patient is diagnosed, and when the model score is less than 0.4911, the aortic valve calcified stenosis patient is not diagnosed.
The ROC curve was plotted using the model score SPSS22.0 software for the healthy control group (50 cases) and the valvular calcified stenosis group (50 cases), and the specific result is shown in fig. 8, and the auc value is 0.907. When the optimal critical point of the model is 0.4911, the sensitivity of the training set is 90.0%, and the specificity is 90.0%.
Therefore, the inflammatory protein scoring model can be used for calculating model scoring values, and the patient with aortic valve calcified stenosis can be diagnosed as follows:
Detecting the content of inflammatory proteins (MMP 1 and SIRT 2) in the plasma of a subject, calculating a model grading value through the inflammatory protein grading model, and diagnosing or candidate diagnosing the subject as an aortic valve calcified stenosis patient if the model grading value of the subject is more than or equal to 0.4911; if the subject model score value is less than 0.4911, the subject diagnosis or candidate diagnosis is not an aortic valve calcified stenosis patient.
Or, a subject with a large model score has a probability of suffering from an aortic valve calcified stenosis greater than or a candidate greater than a subject with a small model score.
The subject may be a patient suspected of having calcified aortic stenosis.
2. Efficacy assessment of inflammatory protein scoring model
1. Detection of the amount of inflammatory proteins (MMP 1 and SIRT 2) in combination
The healthy control group (50 cases) and the valvular calcification stenosis group (50 cases) are used as verification sets, and the content of MMP1 and SIRT2 in blood plasma of each sample in the verification sets is detected by adopting the method 1 of the first step.
2. Application of inflammatory protein scoring model
The contents of MMP1 and SIRT2 obtained in the above 1 are respectively brought into the formula of the inflammatory protein scoring model prepared in the above 2, and model scoring values are calculated. Specific results as shown in the model score dotted graph of fig. 9, in the verification set, the model score mean of the healthy control group was 0.2460, the model score mean of the valvular calcified stenosis group was 0.7006, and the inflammation score of the valvular calcified stenosis group was significantly higher than that of the healthy control group (P < 0.01).
And diagnosing the patient with the aortic valve calcified stenosis when the model score value is more than or equal to 0.4911.
The content and model scoring values of MMP1 and SIRT2 of the two groups of subjects are respectively used for preparing an ROC curve by SPSS 22.0 software, AUC values are calculated, and the scoring models of MMP1, SIRT2 and inflammatory proteins of the subjects are further evaluated according to the area AUC under the working curve of the subjects in time dependence.
As a result, as can be seen in fig. 10, the area under the curve of MMP1 of the validation set is 0.795 (P < 0.001); the area under the curve of SIRT2 is 0.815 (P < 0.001); inflammatory protein scores were 0.892 (P < 0.001) in area under the curve of the validation set. When the model score value is greater than or equal to 0.4911, the sensitivity at the validation set is 78.0% and the specificity is 86.0%.
As a result, the inflammatory protein scoring model as a whole was found to perform well in the validation set, compared to the diagnostic efficacy of MMP1 and SIRT2 alone.
3. Correlation analysis of inflammatory protein scoring model and aortic valve stenosis degree
Using Spearman bivariate correlation analysis, the analysis verified the correlation of the concentrated inflammatory protein model score value with the extent of aortic valve stenosis (maximum aortic cross-valve flow rate) in patients with aortic valve calcified stenosis, all statistical tests were double-tailed, with a P value <0.05 considered to be significantly correlated with the extent of valve stenosis.
The Spearman result is shown in a correlation scatter diagram of fig. 11, the ordinate is the value of the inflammatory protein model score, the abscissa is the maximum aortic valve crossing flow rate of the aortic valve calcified stenosis patients in the training set, the correlation coefficient r=0.348 (p=0.013), the result shows that the value of the inflammatory protein model score is positively correlated with the aortic valve calcified stenosis degree, and the higher the value of the inflammatory protein model score is, the higher the stenosis degree of the patients is, so the inflammatory protein model can be used for evaluating or assisting in evaluating the aortic valve stenosis degree.
Example 3 preparation and application of visual alignment of inflammatory protein scoring model
1. Preparation of visual nomograms
The inflammatory protein scoring model obtained in example 2 one, 2, was visualized as a nomogram. The alignment chart converts a complex regression equation into a visualized graph, so that the result of the prediction model is more readable, and the patient can be conveniently evaluated. Owing to the characteristic of the nomogram which is visual and easy to understand, the nomogram is also gradually and increasingly focused and applied in medical research and clinical practice.
The basic principle of the nomogram is that a multi-factor regression model is constructed, each value level of each influence factor is assigned according to the contribution degree (the size of regression coefficient) of each influence factor in the model, then the scores are added to obtain a total score, and finally the predicted value of the final event of the individual is calculated through the function conversion relation between the total score and the occurrence probability of the final event.
The visual alignment is shown in fig. 12, with the scores corresponding to the MMP1 content in the second row or SIRT2 content in the third row of the first row; the second row of MMP1 is the content of MMP1 protein in the subject; the third row of SIRT2 is the content of SIRT2 protein in the subject; the fourth behavior adds the score of the first line corresponding to the second line level and the score of the first line corresponding to the third line level to obtain a total score; inflammatory protein score values corresponding to the total score of the fourth row of the fifth row; the main variables are the contents of MMP1 and SIRT2, the contents can be corresponding to each score, the scores of all the variables are added to obtain a total score, the total score is drawn downwards to be a vertical line, and the value of inflammatory protein score of a corresponding patient is the probability of the patient suffering from aortic valve calcified stenosis. The greater the score, the greater the probability that the patient is prompted to suffer from calcified stenosis of the aortic valve, and the more need for further diagnosis by imaging.
2. Application of visual alignment chart
The contents of MMP1 and SIRT2 in the healthy control group (50 cases) and the valvular calcification stenosis group (50 cases) in the training set were subjected to the visual alignment chart shown in fig. 12 to obtain the inflammatory protein score values, and the specific results were consistent with fig. 7.
The model scoring values of the two groups of subjects are plotted by SPSS 22.0 software to obtain an optimal critical point (cut-off), a threshold value of the model scoring is calculated to be 0.4911, and when the model scoring value is greater than or equal to 0.4911, the model scoring values of the two groups of subjects are the aortic valve calcified stenosis patients, that is, the greater the probability that the patients suffer from the aortic valve calcified stenosis, the further diagnosis by performing cardiac ultrasound or cardiac CT examination is recommended.
Therefore, the inflammatory protein scoring model can be used for calculating model scoring values, and the patient with aortic valve calcified stenosis can be diagnosed as follows:
detecting the content of inflammatory proteins MMP1 and SIRT2 in the plasma of a subject, calculating an inflammatory protein score value through a visual alignment chart shown in fig. 12, and diagnosing or candidate diagnosing the subject as an aortic valve calcified stenosis patient if the inflammatory protein score value of the subject is more than or equal to 0.4911; if the subject's inflammatory protein score value is less than 0.4911, the subject's diagnosis or candidate diagnosis is not an aortic valve calcified stenosis patient.
Or, a subject with a large inflammatory protein score has a greater probability of suffering from aortic valve calcification stenosis than or a candidate for a subject with a small inflammatory protein score.
The subject may be a patient suspected of having calcified aortic stenosis.
Claims (10)
1. Use of a substance that detects the content of inflammatory proteins MMP1 and SIRT2 for the preparation of a product having at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve.
2. Use of a substance that detects the content of inflammatory proteins MMP1 and SIRT2 and a mediator that supports the following inflammatory protein scoring model formula or a visual nomogram thereof in the preparation of a product having at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from aortic valve calcification stenosis;
the inflammatory protein scoring model formula is shown in the following formula I:
Model score = (Formula I);
Wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant.
3. A kit comprising the agent for detecting the content of inflammatory proteins MMP1 and SIRT2 according to claim 1 or 2.
4. A kit according to claim 3, wherein: the kit further comprises a medium loaded with the following inflammatory protein scoring model formula or a visual nomogram thereof;
the inflammatory protein scoring model formula is shown in the following formula I:
Model score = (Formula I);
wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant.
5. The kit of claim 3 or 4, wherein:
the kit has at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve.
6. An apparatus for diagnosing or assisting in diagnosing aortic valve calcification stenosis comprises a data receiving module, a data processing module and a data output module;
The data receiving module is used for receiving the content of MMP1 and SIRT2 proteins in the blood plasma of a subject;
The data processing module is used for substituting the content of MMP1 and SIRT2 proteins into an inflammatory protein scoring model to obtain the inflammatory protein scoring value of the subject,
The inflammatory protein scoring model is the following formula or a visual alignment chart thereof;
the formula of the inflammatory protein scoring model is shown in the following formula I:
Model score = (Formula I);
wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
X (MMP1) is the content of MMP1, X (SIRT2) is the content of SIRT2, and e is a natural constant;
The data output module is used for outputting whether the subject suffers from the aortic valve calcification stenosis or the aortic valve calcification stenosis degree result according to the inflammatory protein score value.
7. A computer apparatus comprising a memory, a processor, and a computer program stored on the memory, characterized by: the processor executes the computer program to implement the steps of:
S1) data receiving: receiving the levels of MMP1 and SIRT2 proteins in the plasma of the subject;
S2) data processing: substituting the content of MMP1 and SIRT2 proteins into an inflammatory protein scoring model to obtain an inflammatory protein scoring value of a subject; the prognosis risk prediction model is as follows formula 1:
the inflammatory protein scoring model is the following formula or a visual alignment chart thereof;
the formula of the inflammatory protein scoring model is shown in the following formula I:
Model score = (Formula I);
wherein logic (P) = -11.206+0.035 x (MMP1)+ 0.283*X(SIRT2);
x (MMP 1) is the content of MMP1, X (SIRT 2) is the content of SIRT2, and e is a natural constant;
s3) data output: outputting whether the subject suffers from the aortic valve calcification stenosis or the aortic valve calcification stenosis degree result according to the inflammatory protein score value.
8. A computer-readable storage medium having stored thereon a computer program, characterized by: the computer program realizes the steps in claim 7 when executed by a processor.
9. A computer program product comprising a computer program characterized by: which computer program, when being executed by a processor, carries out the steps of claim 7.
10. Use of a substance for detecting the content of inflammatory proteins MMP1 and SIRT2 as defined in claim 1 or 2, a kit as defined in any one of claims 3 to 5 or a device as defined in claim 6 or a computer device as defined in claim 7 or a computer readable storage medium as defined in claim 8 or a computer program product as defined in claim 9 or a product as defined in claim 1 or 2 as a detection target for the preparation of a product having at least one of the following functions:
1) Diagnosing or aiding in diagnosing aortic valve calcification stenosis;
2) Screening or assisting in screening aortic valve calcification stenosis;
3) Assessing or aiding in assessing the extent of calcified stenosis of the aortic valve;
4) Predicting or assisting in predicting the probability of suffering from calcified stenosis of the aortic valve.
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