CN118150818A - End-stage renal disease cardiovascular and cerebrovascular event prediction method based on plasma lipidomic and erythrocyte lipidomic - Google Patents
End-stage renal disease cardiovascular and cerebrovascular event prediction method based on plasma lipidomic and erythrocyte lipidomic Download PDFInfo
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Abstract
The invention discloses a method for predicting cardiovascular and cerebrovascular events of end-stage renal disease based on plasma lipidomic and erythrocyte lipidomic, in particular relates to the application of biomarkers PC42:6, PC42:7 and SM d18:1/14:0, the biomarkers also comprise new cardiovascular and cerebrovascular event index age, sex and dialysis year, the invention provides the application of a reagent for detecting the biomarkers in preparing a product for assisting in diagnosing or predicting whether the end-stage renal disease is combined with cardiovascular and cerebrovascular diseases, the invention provides a product for assisting in diagnosing or predicting whether the end-stage renal disease is combined with cardiovascular and cerebrovascular diseases, the invention also provides the application and construction method of a related computer model, and the application of the biomarkers in a system for automatically assisting in diagnosing or predicting whether the end-stage renal disease is combined with cardiovascular and cerebrovascular diseases.
Description
Technical Field
The invention belongs to the technical field of biology, relates to a method for predicting end-stage renal disease cardiovascular and cerebrovascular events based on plasma lipidomic and erythrocyte lipidomic, and particularly relates to biomarkers PC42:6, PC42:7 and SM d18:1/14:0.
Background
End Stage Renal Disease (ESRD) refers to the end stage of various chronic kidney diseases, and is a current global significant public health problem with high morbidity and heavy disease burden. Serious electrolyte and hemodynamic disturbances occur in ESRD patients with a corresponding increased risk of developing cardiovascular and cerebrovascular disease (CVD). Hemodialysis is an important treatment method for ESRD patients, and electrolyte and hemodynamics are changed severely in the hemodialysis process, so that the risks of various cardiovascular and cerebrovascular diseases are further increased finally, and sudden death even occurs. Thus, prediction of CVD risk in ESRD patients is of paramount importance. Currently, there is no good prediction of the presence or absence of CVD in ESRD patients under dialysis.
Disclosure of Invention
In order to solve the technical problems in the prior art, the invention provides the following technical scheme:
The invention provides application of a reagent for detecting biomarkers in a sample in preparation of a product for assisting diagnosis or prediction of whether end-stage renal disease is combined with cardiovascular and cerebrovascular diseases, wherein the biomarkers comprise PC42:6, PC42:7 and SM d18:1/14:0.
Further, the biomarkers include combinations of PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including age, sex, year of dialysis.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including combinations of age, gender, year of dialysis.
Sphingomyelin (d18:1/14:0) or SM (d18:1/14:0) is a type of sphingolipid found in animal cell membranes, particularly in the myelin sheath of the membrane around some nerve cell axons. It generally consists of phosphorylcholine and ceramide. SM (d18:1/14:0) consists of a sphingosine backbone and a myristic acid chain. In humans, sphingomyelin is the only membrane phospholipid that is not derived from glycerol. Like all sphingolipids, SM has a ceramide core (sphingosine is bound to fatty acids via amide bonds). In addition, it contains a polar head group, namely phosphorylcholine or phosphoethanolamine.
PC42:6 and PC42:7 belong to phosphatidylcholine (PC or GPCho), are one kind of phospholipids, and the head group is choline group, which is an important constituent of biological membranes. As in the case of diacylglycerols, glycerophosphorylcholine can have many different combinations of fatty acids of different lengths and saturation attached at the C-1 and C-2 positions. The sum of the lengths of the two fatty acid chains connected by PC42:6 is 42, the sum of the unsaturations is 6, the sum of the lengths of the two fatty acid chains connected by PC42:7 is 42, and the sum of the unsaturations is 7.
Further, the end stage renal disease includes the end stage of chronic kidney disease including primary glomerulonephritis, hypertensive glomerulonephritis, diabetic nephropathy, secondary glomerulonephritis, tubular interstitial lesions, ischemic kidney disease, hereditary kidney disease.
Further, the tubular interstitial lesions include acute and chronic interstitial nephritis, chronic pyelonephritis, chronic uric acid nephropathy, obstructive nephropathy, and pharmaceutical nephropathy.
Further, the hereditary kidney disease includes polycystic kidney disease and hereditary nephritis.
Further, the cardiovascular and cerebrovascular diseases include cardiovascular diseases and cerebrovascular diseases.
Further, the cardiovascular diseases include atherosclerosis, ischemic heart disease, coronary heart disease, cardiac insufficiency, peripheral arterial disease, coronary artery bypass grafting, unstable angina, unstable refractory angina, stable angina, chronic stable angina, acute coronary syndrome, and myocardial infarction.
Further, the myocardial infarction includes primary or recurrent myocardial infarction, non-Q-wave type myocardial infarction, non-ST elevation type myocardial infarction, ST elevation type myocardial infarction.
The term "cardiovascular disease" refers to a disease that affects the heart or blood vessels, or both. In particular, cardiovascular diseases include cardiac arrhythmias (atrial or ventricular or both); atherosclerosis and its sequelae; angina pectoris; heart rhythm disorders; myocardial ischemia; myocardial infarction; cardiac or vascular aneurysms; stroke; peripheral obstructive arterial disease of a limb, organ or tissue; reperfusion injury after ischemia of brain, heart, kidney or other organ or tissue; endotoxic, surgical or traumatic shock; hypertension, valvular heart disease, heart failure, abnormal blood pressure; vasoconstriction (including those associated with migraine); vascular abnormalities, inflammation or dysfunction limited to a single organ or tissue.
Further, the cerebrovascular diseases include acute cerebral infarction, cerebral hemorrhage, cerebral apoplexy, cerebral lacunar infarction, cerebral micro hemorrhage, and leukoencephalopathy.
Further, the sample includes a tissue sample, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, blood, vitreous humor, lymph, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, ascites, pelvic rinse, gynecological fluid, pleural fluid, tumor lysates, thyroid tissue, tissue culture fluid, tissue extracts, homogenized tissue, tumor tissue, cell extracts.
Further, the sample includes serum, plasma, blood-derived cells, platelets.
Further, the sample includes serum, blood, and plasma.
Further, the sample is blood.
The term "sample" is used in its broadest sense. In one sense, it may refer to animal cells or tissues. In another sense, it is meant to include samples or cultures obtained from any source, as well as biological and environmental samples. Biological samples may be obtained from plants or animals (including humans) and include fluids, solids, tissues, and gases. In some embodiments, the sample is a blood sample.
Further, the reagent includes a reagent for detecting the expression level of the biomarker by western blotting, ELISA, radioimmunoassay, oxter lony immunodiffusion method, rocket electrophoresis method, tissue immunostaining method, immunoprecipitation assay, complement fixation assay, FACS, and protein chip assay.
Further, the product comprises a kit, a chip, test paper, a nucleic acid membrane strip, an antibody and a ligand.
The term "biomarker" refers to a factor that is a unique indicator of a biological process, biological event, and/or pathological condition, and the term biomarker encompasses both clinical markers and biomarkers. Thus, in the context of the present invention, the term "biomarker" encompasses, for example, "biological biomarkers", the biological markers disclosed herein also include genes encoding those proteins (DNA and/or RNA) as well as metabolites. The term "biomarker" also encompasses "clinical biomarkers" (also referred to as "clinical status markers") that can predict a response to a biological therapy, such as gender, age, concomitant medication, smoking status, body Mass Index (BMI), and the like. See, for example, U.S. publications US20150065530, US20140141990, US20130005596, US20090233304, US20140199709, US20130303398, US20110212104, which are incorporated herein by reference in their entirety.
The invention provides a product for assisting in diagnosing or predicting whether end-stage renal disease is combined with cardiovascular and cerebrovascular diseases, the product comprises the reagent for detecting biomarkers in a sample, wherein the biomarkers comprise PC42:6, PC42:7 and SM d18:1/14:0.
Further, the biomarkers include combinations of PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including age, sex, year of dialysis.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including combinations of age, gender, year of dialysis.
Further, the new cardiovascular and cerebrovascular events include new cardiovascular events and new cerebrovascular events.
Further, the new cardiovascular event includes atherosclerosis, ischemic heart disease, coronary heart disease, cardiac insufficiency, peripheral arterial disease, coronary artery bypass grafting, unstable angina, unstable refractory angina, stable angina, chronic stable angina, acute coronary syndrome, myocardial infarction.
Further, the myocardial infarction includes primary or recurrent myocardial infarction, non-Q-wave type myocardial infarction, non-ST elevation type myocardial infarction, ST elevation type myocardial infarction.
Further, the new-onset cerebrovascular events include cerebral hemorrhage, cerebral infarction, myocardial infarction, cerebral microhemorrhage, and leukoencephalopathy.
The term "diagnosis" refers to detecting a disease or determining the stage or extent of a disease. Typically, diagnosis of a disease is based on an assessment of one or more factors and/or symptoms predictive of the disease. That is, diagnosis may be based on the presence, absence, or amount of an agent that indicates the presence or absence of a predicted disease or disorder. It is contemplated that each factor or symptom indicative of diagnosis of a particular disease need not be exclusively related to a particular disease, e.g., there may be differential diagnosis that may be inferred from diagnostic factors or symptoms. Likewise, there may be instances where factors or symptoms indicative of a particular disease are present in individuals without the particular disease. The term "diagnosis" also encompasses determining the therapeutic effect of a drug therapy, or predicting the pattern of response to a drug therapy. Diagnostic methods may be used alone or in combination with other diagnostic and/or staging methods known in the medical arts for a particular disease.
The invention provides application of a biomarker in constructing a computer model for assisting diagnosis or predicting whether end-stage renal disease is combined with cardiovascular and cerebrovascular diseases, wherein the biomarker comprises the biomarker.
The invention provides a method for constructing an end-stage renal disease combined cardiovascular and cerebrovascular disease auxiliary diagnosis or prediction model based on biomarkers of plasma lipidomic and erythrocyte lipidomic, which comprises the steps of performing model training by using a machine learning method, and constructing to obtain a prediction model, wherein the biomarkers comprise the biomarkers.
Further, the construction method comprises the steps of sample collection and processing of the biomarker.
Further, the samples of the biomarkers are from end stage renal patients who have and have not had cardiovascular and cerebrovascular diseases.
Further, the machine learning method comprises a decision tree model, a random forest model, a K-nearest neighbor algorithm model, a naive Bayesian model, a support vector machine model and a neural network model.
Further, the machine learning method uses the expression level of the biomarker as a feature.
The invention provides application of a biomarker in a system for automatically assisting in diagnosing or predicting whether end-stage renal disease is combined with cardiovascular and cerebrovascular diseases, wherein the biomarker comprises PC42:6, PC42:7 and SM d18:1/14:0.
Further, the biomarkers include combinations of PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including age, sex, year of dialysis.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including combinations of age, gender, year of dialysis.
The invention provides a system for automatically assisting in diagnosing or predicting whether end-stage renal disease is complicated with cardiovascular and cerebrovascular diseases, which comprises a result judging module, wherein the result judging module is used for analyzing whether the end-stage renal disease of a subject is complicated with cardiovascular and cerebrovascular diseases according to the expression level of the biomarker.
Further, the system comprises an input module for inputting the expression level of the aforementioned biomarker.
Further, the system comprises an output module for outputting the analysis result of the result judging module.
The term "automated" refers to the situation where it is not necessary to perform any one or more steps of a procedure or method (e.g. cell collection) by hand (i.e. manually), but where the desired step or procedure (e.g. cell collection) may be performed by one or more suitable devices or systems such as one or more robots, liquid handling robots and/or other (e.g. solvent formulation) liquid transfer devices which are programmed prior to performing the one or more steps such that the corresponding steps and modes (in particular the collection steps) are performed in an automated manner according to the programmed content.
The term "system" refers not only to a system having a configuration in which a plurality of computers, pieces of hardware, devices, and the like are connected to each other via a communication unit such as a network (including communication connection established in a one-to-one manner), but also to a system implemented by one computer, one piece of hardware, one device, and the like.
The term "subject" or "patient" includes any human or non-human animal. The term "non-human animal" includes all vertebrates, e.g., mammals and non-mammals, such as non-human primates, sheep, dogs, cats, horses, cows, bears, chickens, amphibians, reptiles, and the like.
In some embodiments, implementation of the system includes performing or completing selected tasks manually, automatically, or a combination thereof. Moreover, the actual instrumentation and equipment of the embodiments of the systems according to the present invention may utilize the systems to implement various program tasks by way of hardware, software, firmware, or a combination thereof.
The invention provides application of a biomarker in preparing a product for end-stage renal disease typing, wherein the end-stage renal disease typing comprises end-stage renal disease complicated with cardiovascular and cerebrovascular diseases and end-stage renal disease not complicated with cardiovascular and cerebrovascular diseases.
The biomarkers include PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers include combinations of PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including age, sex, year of dialysis.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including combinations of age, gender, year of dialysis.
The invention provides a construction method of a product for end-stage renal disease typing, which comprises the following steps:
1) Collecting samples of patients with end-stage renal disease, and respectively detecting the expression levels of the biomarkers in the samples;
2) Comparing the detection result in the step 1) with the critical expression level of the biomarker, and distinguishing the end-stage renal patients into end-stage renal disease complicated with cardiovascular and cerebrovascular diseases or end-stage renal disease not complicated with cardiovascular and cerebrovascular diseases according to the result;
the biomarkers include PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers include combinations of PC42:6, PC42:7, SM d18:1/14:0.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including age, sex, year of dialysis.
Further, the biomarkers also include new cardiovascular and cerebrovascular event prediction/diagnosis indicators including combinations of age, gender, year of dialysis.
Drawings
FIG. 1 is a ROC graph tested for end stage renal disease combined with cardiovascular and cerebrovascular disease diagnosis using a control base model and an experimental lipid model;
Fig. 2 is a ROC graph demonstrating whether end stage renal disease is diagnosed with cardiovascular and cerebrovascular disease centrally using a control base model and an experimental lipid model.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described herein.
The logarithmic function used to correlate marker combinations with disease preferably employs algorithms developed and obtained by applying statistical methods. For example, suitable statistical methods are Discriminant Analysis (DA) (i.e., linear, quadratic, regular DA), kernel method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based method (i.e., logistic regression, CART, random forest method), generalized linear model (i.e., logistic regression), principal component-based method (i.e., SIMCA), generalized superposition model, fuzzy logic-based method, neural network and genetic algorithm-based method. The skilled artisan will not have problems in selecting an appropriate statistical method to evaluate the marker combinations of the present invention and thereby obtain an appropriate mathematical algorithm.
Subject work characteristics curve (receiver operating characteristic curve, ROC curve for short): is a curve plotted on the ordinate with sensitivity (true positive rate) and 1-specificity (false positive rate) on the abscissa, according to a series of different classification schemes (demarcation values or decision thresholds). The area under the ROC curve is an important test accuracy index, and the larger the area under the ROC curve is, the larger the diagnostic value of the test is.
The term "AUC" is an abbreviation for "area under the curve". In particular, the area under the subject's operating characteristics (ROC) curve. ROC curves are curves of true positive versus false positive for different possible cut-points of a diagnostic test. The trade-off between sensitivity and specificity is shown to depend on the cut-off chosen (any increase in sensitivity is accompanied by a decrease in specificity). The area under the ROC curve (AUC) is a measure of the accuracy of the diagnostic test (the larger the area the better; the optimal value is 1; the ROC curve for random examination is on the diagonal and the area is 0.5; see j.p.egan. (1975) signal detection theory and ROC analysis, ACADEMIC PRESS, new York). Examples biomarkers and end stage renal disease whether combined diagnosis and prognosis of cardiovascular and cerebrovascular disease is relevant
1. Experimental objects
In the experiment, the sample sources for the test set and the verification set are clinical collection samples, the samples are all from end-stage renal patients, and the samples are all blood samples. The test set collects 58 samples, the verification set collects 59 samples, and the significant difference analysis and the ROC analysis are respectively carried out.
Inclusion exclusion criteria for the end stage renal patients were as follows:
Patients who were derived from Beijing co-ordination hospitals and who were involved in CKD 4-5 phase and regular maintenance dialysis treatment from 7 months in 2013 to 7 months in 2014.
Group entry criteria: (1) aged 17 years or older; (2) the maintenance dialysis patient needs regular dialysis for more than 2 months; (3) agree and sign an informed consent form.
Exclusion criteria: (1) Systemic infection, acute cardiovascular events, malignancy, surgery or trauma 1 month prior to inclusion in the study; (2) Mental diseases, mood disorder, long-term administration of mental disease drug, epilepsy, and dementia patients; (3) metabolic encephalopathy; (4) Non-atherogenic vascular disease (e.g., arteritis) history.
The basic information of 117 end stage renal patients is shown in Table 1:
TABLE 1
2. Experimental method
1) The experimental sample extraction principle is that repeated 2000 random samplings are used to divide the experimental sample extraction principle into 2 groups of samples, wherein each group accounts for 50%, and 58 samples of the test set and 59 samples of the verification set are finally obtained.
2) Control base model composition: new-onset cardio-cerebrovascular events ' -age+Gender+ ' DIALYSIS VINTAGE ' (i.e. "New cardiovascular and cerebrovascular events" -age+sex+ "dialysis year")
Experimental lipid model composition ：`New-onset cardio-cerebrovascular events`～Age+Gender+`Dialysis vintage`+`PC42:6`+`PC42:7`+`SM d18:1/14:0`( namely "New cardiovascular and cerebrovascular event" -age+sex+ "dialysis year" + "PC42:6" + "PC42:7" + "SM d18:1/14:0")
3) Blood lipidomic method
Lipids were extracted from blood samples using the modified Bligh/dyr extraction method (two extractions), i.e. blood samples were supplemented with 750 μl of chloroform: methanol (1:2) (v/v) followed by incubation at 1500rpm for 30min at 4 ℃. After the incubation was completed, phase separation was induced by adding 350 μl deionized water and 250 μl chloroform. The lipid-containing lower organic phase was extracted into clean tubes. The lipid extraction was repeated once, 450 μl of chloroform was added to the remaining aqueous phase, the lipid extracts were pooled in a single tube and dried in OH mode in SpeedVac. The samples were stored at-80℃for further analysis.
The samples were reconstituted in an isotope mix and all analyses were performed in electrospray ionization (ESI) mode using a Exion UPLC-QTRAP 6500Plus (Sciex) liquid chromatography-mass spectrometer under the following conditions: curtaingas= 20,ion spray voltage =5500 v, temperature=400 ℃, and ion source gas 1= 35,ion source gas 2 =35.
Using Phenomenex Luna silica μm (inner diameter 150x2.0 mm) chromatography column, the various polar lipids were separated under the following conditions: mobile phase a (chloroform: methanol: ammonia 89.5:10:0.5) and mobile phase B (chloroform: methanol: ammonia 55:39:0.5:5.5) were mixed with water. The mobile phase a gradient was held for 5min starting at 95% and then linearly decreasing to 60% and holding for 4min in 7min, then mobile phase a was further decreased to 30% and holding for 15min. Finally, the initial gradient was maintained for 5min. Mass spectrometry multi-reaction monitoring (multiple reaction monitoring, MRM) was established for various lipid identification and quantitative analysis. The quantification of the lipid material was performed by means of an added internal standard.
4) Differential expression analysis
And carrying out differential expression analysis on the test set and the verification set according to the detection result of the blood lipidomic of the biomarker to obtain the respective model regression coefficients of the test set and the verification set.
5) Diagnostic efficacy analysis
Drawing a working curve (ROC) of a subject by adopting an R package 'pROC', and analyzing the AUC values of the biomarker obtained by screening in a test set and a verification set to judge whether the biomarker has diagnosis efficacy on end-stage renal disease combined with cardiovascular and cerebrovascular diseases.
6) Statistical analysis method
The expression differences of the biomarkers were assessed using the Wilcoxon signed rank sum test. The remaining data were analyzed using Student's t test (normal distribution variable) or Wilcoxon rank sum test (non-normal distribution variable). All statistical tests were performed with R (version 3.6.3) and the significance threshold was set to 0.05.
3. Experimental results
1) The results of the differential expression analysis of the test set are shown in table 2:
TABLE 2
2) The results of the differential expression analysis of the validation set are shown in table 3:
TABLE 3 Table 3
| Group of | estimate | std.error | statistic | p.value |
| Experimental lipid model | -6.153320878 | 2.923819682 | -2.104548689 | 0.035330612 |
| Age of | 0.059568104 | 0.040691942 | 1.463879615 | 0.143226844 |
| Sex (sex) | 1.199998623 | 0.873752889 | 1.373384441 | 0.169632864 |
| Dialysis year | 0.004911842 | 0.007549016 | 0.650659931 | 0.515266034 |
| `PC42:6` | 0.909318077 | 0.688092678 | 1.321505236 | 0.186332958 |
| `PC42:7` | 0.504923033 | 0.609230856 | 0.828787689 | 0.407224556 |
| `SM d18:1/14:0` | 0.540634757 | 0.428701899 | 1.261097182 | 0.207273836 |
3) ROC curve analysis for diagnosing or predicting whether end-stage renal disease is combined with cardiovascular and cerebrovascular diseases by using biomarker
The result is shown in figure 1, and the result shows that the AUC value of the ROC curve obtained by the control basic model is 0.70, the AUC value of the ROC curve obtained by the experimental lipid model is 0.85, and the difference between the AUC value and the AUC value is obvious (p=0.022), so that the experimental lipid model can improve the accuracy of diagnosing or predicting whether the end-stage renal disease is combined with the cardiovascular and cerebrovascular diseases, and the biomarker of the experimental lipid model can be used as a biomarker for diagnosing or predicting whether the end-stage renal disease is combined with the cardiovascular and cerebrovascular diseases.
The result is shown in figure 2, and the result shows that the AUC value of the ROC curve obtained by the control basic model is 0.65, the AUC value of the ROC curve obtained by the experimental lipid model is 0.85, the difference between the AUC value and the AUC value is obvious (p=0.013), and the experimental lipid model is verified to be capable of improving the accuracy of diagnosing or predicting whether the end-stage renal disease is combined with the cardiovascular and cerebrovascular diseases, and the biomarker of the experimental lipid model is proved to be a biomarker for diagnosing or predicting whether the end-stage renal disease is combined with the cardiovascular and cerebrovascular diseases.
The above description of the embodiments is only for the understanding of the method of the present invention and its core ideas. It should be noted that it will be apparent to those skilled in the art that several improvements and modifications can be made to the present invention without departing from the principle of the invention, and these improvements and modifications will fall within the scope of the claims of the invention.
Claims (10)
1. The application of a reagent for detecting a biomarker in a sample in preparing a product for assisting diagnosis or predicting whether end-stage renal disease is combined with cardiovascular and cerebrovascular diseases is characterized in that the biomarker comprises PC42:6, PC42:7, SMd18:1/14:0;
Preferably, the biomarkers comprise a combination of PC42:6, PC42:7, SMd 18:18:1/14:0;
preferably, the biomarker further comprises a new cardiovascular and cerebrovascular event prediction/diagnosis index, wherein the index comprises age, gender and dialysis year;
Preferably, the biomarker further comprises a new cardiovascular event prediction/diagnosis index comprising a combination of age, sex, year of dialysis.
2. The use according to claim 1, wherein the end stage renal disease comprises the end stage of chronic kidney disease including primary glomerulonephritis, hypertensive arteriole sclerosis, diabetic nephropathy, secondary glomerulonephritis, tubular interstitial lesions, ischemic kidney disease, hereditary kidney disease;
Preferably, the tubular interstitial lesions include chronic pyelonephritis, chronic uric acid nephropathy, obstructive nephropathy, and drug-induced nephropathy;
preferably, the hereditary kidney disease comprises polycystic kidney disease and hereditary nephritis;
preferably, the cardiovascular and cerebrovascular diseases include cardiovascular diseases and cerebrovascular diseases;
Preferably, the cardiovascular disease comprises atherosclerosis, ischemic heart disease, coronary heart disease, cardiac insufficiency, peripheral arterial disease, coronary artery bypass grafting, unstable angina, unstable refractory angina, stable angina, chronic stable angina, acute coronary syndrome, myocardial infarction;
preferably, the myocardial infarction comprises primary or recurrent myocardial infarction, Q-wave-free myocardial infarction, non-ST elevation myocardial infarction and ST elevation myocardial infarction;
preferably, the cerebrovascular disease comprises acute cerebral infarction, cerebral hemorrhage, cerebral apoplexy, cerebral lacunar infarction, cerebral micro hemorrhage, and leukoencephalopathy.
3. The use according to claim 1, wherein the sample comprises a tissue sample, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, blood, vitreous humor, lymph, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood derived cells, urine, cerebral spinal fluid, saliva, sputum, tears, sweat, mucus, ascites, pelvic flushing fluid, gynecological fluid, pleural fluid, tumor lysates, thyroid tissue, tissue culture fluid, tissue extracts, homogenized tissue, tumor tissue, cell extracts;
Preferably, the sample comprises serum, plasma, blood-derived cells, platelets;
Preferably, the sample is blood;
Preferably, the reagent comprises a reagent for detecting the expression level of a biomarker by western blotting, ELISA, radioimmunoassay, oxter lony immunodiffusion method, rocket electrophoresis method, tissue immunostaining method, immunoprecipitation assay, complement fixation assay, FACS, protein chip assay;
preferably, the product comprises a kit, a chip, a test paper, a nucleic acid membrane strip, an antibody, a ligand.
4. A product for aiding in the diagnosis or prognosis of end stage renal disease combined with cardiovascular and cerebrovascular disease, comprising a reagent according to any one of claims 1 to 3 for detecting a biomarker in a sample, the biomarker comprising PC42:6, PC42:7, SMd18:1/14:0;
Preferably, the biomarkers comprise a combination of PC42:6, PC42:7, SMd 18:18:1/14:0;
preferably, the biomarker further comprises a new cardiovascular and cerebrovascular event prediction/diagnosis index, wherein the index comprises age, gender and dialysis year;
Preferably, the biomarker further comprises a new cardiovascular event prediction/diagnosis index comprising a combination of age, sex, year of dialysis.
5. Use of a biomarker in the construction of a computer model for aiding in the diagnosis or prognosis of end stage renal disease in combination with cardiovascular and cerebrovascular disease, wherein the biomarker comprises the biomarker of claim 4.
6. A method for constructing an end-stage renal disease combined cardiovascular and cerebrovascular disease auxiliary diagnosis or prediction model based on biomarkers of plasma lipidomic and erythrocyte lipidomic, which is characterized in that the method comprises performing model training by using a machine learning method, and constructing a prediction model, wherein the biomarkers comprise the biomarkers of claim 4;
preferably, the construction method comprises the steps of sample collection and processing of the biomarker;
Preferably, the samples of the biomarkers are from end stage renal patients who have and do not have cardiovascular and cerebrovascular diseases;
preferably, the machine learning method comprises a decision tree model, a random forest model, a K-nearest neighbor algorithm model, a naive Bayesian model, a support vector machine model and a neural network model;
Preferably, the machine learning method uses the expression level of the biomarker as a feature.
7. Use of a biomarker in a system for automated assisted diagnosis or prediction of whether end stage renal disease incorporates cardiovascular and cerebrovascular disease, wherein the biomarker comprises PC42:6, PC42:7, SMd18:1/14:0;
Preferably, the biomarkers comprise a combination of PC42:6, PC42:7, SMd 18:18:1/14:0;
preferably, the biomarker further comprises a new cardiovascular and cerebrovascular event prediction/diagnosis index, wherein the index comprises age, gender and dialysis year;
Preferably, the biomarker further comprises a new cardiovascular event prediction/diagnosis index comprising a combination of age, sex, year of dialysis.
8. A system for automated assisted diagnosis or prediction of whether end stage renal disease is associated with cardiovascular and cerebrovascular disease, comprising a result determination module for analyzing whether end stage renal disease is associated with cardiovascular and cerebrovascular disease in a subject based on the expression level of the biomarker of claim 7;
preferably, the system comprises an input module for inputting the expression level of the biomarker of claim 7;
Preferably, the system comprises an output module for outputting the analysis result of the result judging module.
9. Use of a biomarker in the manufacture of a product for end-stage renal disease typing, wherein the end-stage renal disease typing comprises end-stage renal disease complicated with cardiovascular and cerebrovascular disease, and end-stage renal disease not complicated with cardiovascular and cerebrovascular disease;
the biomarkers comprise PC42:6, PC42:7, SMd18:1/14:0;
Preferably, the biomarkers comprise a combination of PC42:6, PC42:7, SMd 18:18:1/14:0;
preferably, the biomarker further comprises a new cardiovascular and cerebrovascular event prediction/diagnosis index, wherein the index comprises age, gender and dialysis year;
Preferably, the biomarker further comprises a new cardiovascular event prediction/diagnosis index comprising a combination of age, sex, year of dialysis.
10. A method of constructing a product for end stage renal disease typing, the method comprising:
1) Collecting samples of patients with end-stage renal disease, and respectively detecting the expression levels of the biomarkers in the samples;
2) Comparing the detection result in the step 1) with the critical expression level of the biomarker, and distinguishing the end-stage renal patients into end-stage renal disease complicated with cardiovascular and cerebrovascular diseases or end-stage renal disease not complicated with cardiovascular and cerebrovascular diseases according to the result;
the biomarkers comprise PC42:6, PC42:7, SMd18:1/14:0;
Preferably, the biomarkers comprise a combination of PC42:6, PC42:7, SMd 18:18:1/14:0;
preferably, the biomarker further comprises a new cardiovascular and cerebrovascular event prediction/diagnosis index, wherein the index comprises age, gender and dialysis year;
Preferably, the biomarker further comprises a new cardiovascular event prediction/diagnosis index comprising a combination of age, sex, year of dialysis.
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