CN112763732B - Application of PE (16:0/20:2) and composition thereof in diagnosis of diabetes and diabetic nephropathy - Google Patents

Application of PE (16:0/20:2) and composition thereof in diagnosis of diabetes and diabetic nephropathy Download PDF

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CN112763732B
CN112763732B CN202110204927.7A CN202110204927A CN112763732B CN 112763732 B CN112763732 B CN 112763732B CN 202110204927 A CN202110204927 A CN 202110204927A CN 112763732 B CN112763732 B CN 112763732B
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diabetic nephropathy
tag54
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hcs
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CN112763732A (en
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刘史佳
徐晓燕
徐婷婷
周栋
安晓飞
张露
朱琳
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Jiangsu Provincial Hospital of Chinese Medicine
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/04—Endocrine or metabolic disorders
    • G01N2800/042—Disorders of carbohydrate metabolism, e.g. diabetes, glucose metabolism
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/34—Genitourinary disorders
    • G01N2800/347—Renal failures; Glomerular diseases; Tubulointerstitial diseases, e.g. nephritic syndrome, glomerulonephritis; Renovascular diseases, e.g. renal artery occlusion, nephropathy

Abstract

The invention discloses application of PE (16:0/20:2) and a composition thereof in diagnosis of diabetes and diabetic nephropathy. The serum lipid PE (16:0/20:2) or PE (16:0/20:2) combined with TAG54:2-FA18:1 provided by the invention can diagnose and distinguish healthy people and type 2 diabetes patients or healthy people and early stage diabetic nephropathy or early stage and late stage of diabetic nephropathy, has high diagnosis accuracy, and therefore has a prospect of developing and preparing a reagent or a kit for diagnosing type 2 diabetes, early stage diabetic nephropathy or diagnosing and distinguishing early stage and late stage of diabetic nephropathy.

Description

Application of PE (16:0/20:2) and composition thereof in diagnosis of diabetes and diabetic nephropathy
Technical Field
The invention belongs to the field of biochemistry, relates to application of a metabolic marker in disease diagnosis, and particularly relates to application of PE (16:0/20:2) and a composition thereof in diagnosis of diabetes and diabetic nephropathy.
Background
The biomarkers can be used as signal indicators reflecting the change of organism structure and function, and can be used for detecting the occurrence and the progression of complex diseases. In recent years, biomarkers in the field of omics are used as auxiliary means for judging the occurrence condition of diseases accurately and sensitively in advance, and a good effect is achieved. The combined diagnosis of multiple biomarkers can distinguish the type of the disease and the stage of the disease, and can assist clinical treatment. And taking a serum marker as an example, the method has the advantages of simplicity, rapidness, economy and relative non-invasiveness, is widely adopted and is very friendly to patients.
The invention is especially proposed for developing serum markers or compositions thereof for diagnosing diabetes and diabetic nephropathy.
Disclosure of Invention
The invention aims to provide application of PE (16:0/20:2) and a composition thereof in diagnosis of diabetes and diabetic nephropathy.
The above purpose of the invention is realized by the following technical scheme:
application of serum lipid PE (16:0/20:2) or serum lipid PE (16:0/20:2) in combination with TAG54:2-FA18:1 in preparation of reagents and kits for diagnosing type 2 diabetes.
Application of serum lipid PE (16:0/20:2) or serum lipid PE (16:0/20:2) in combination with TAG54:2-FA18:1 in preparation of reagents and kits for diagnosing early diabetic nephropathy.
The application of serum lipid PE (16:0/20:2) or serum lipid PE (16:0/20:2) in combination with TAG54:2-FA18:1 in preparing reagents and kits for diagnosing and distinguishing early stage and late stage of diabetic nephropathy.
A kit for diagnosing type 2 diabetes, comprising a detection reagent for detecting serum lipid PE (16:0/20:2), or comprising a detection reagent for detecting serum lipid PE (16:0/20:2) and TAG54:2-FA18: 1.
A kit for diagnosing early diabetic nephropathy comprises a detection reagent for detecting serum lipid PE (16:0/20:2) or a detection reagent for detecting serum lipid PE (16:0/20:2) and TAG54:2-FA18: 1.
A kit for diagnosing and distinguishing early stage and late stage of diabetic nephropathy comprises a detection reagent for detecting serum lipid PE (16:0/20:2) or a detection reagent for detecting serum lipid PE (16:0/20:2) and TAG54:2-FA18: 1.
Has the advantages that:
1. the diagnostic index provided by the invention is serum lipid, and the detection can be realized only by adopting a small amount of blood, so that the detection is basically noninvasive;
2. the serum lipid PE (16:0/20:2) or PE (16:0/20:2) combined with TAG54:2-FA18:1 provided by the invention can diagnose and distinguish healthy people and type 2 diabetes patients or healthy people and early stage diabetic nephropathy or early stage and late stage of diabetic nephropathy, has high diagnosis accuracy, and therefore has a prospect of developing and preparing a reagent or a kit for diagnosing type 2 diabetes, early stage diabetic nephropathy or diagnosing and distinguishing early stage and late stage of diabetic nephropathy.
Drawings
FIG. 1 is a graph showing the serum levels of lipid PE (16:0/20:2), TAG54:2-FA18:1 in HCs and 2-DM patients;
FIG. 2 is a graph showing the levels of lipid PE (16:0/20:2), TAG54:2-FA18:1 in serum of patients with HCs and DKDE;
FIG. 3 is the level of lipid PE (16:0/20:2), TAG54:2-FA18:1 in the serum of DKDA and DKDE patients;
FIG. 4 is a ROC curve for PE (16:0/20:2) independent diagnostics 2-DM vs HCs;
FIG. 5 is a ROC curve for PE (16:0/20:2) in combination with TAG54:2-FA18:1 for diagnosis of 2-DM vs HCs;
FIG. 6 is a ROC curve for independent diagnosis of DKDE vs HCs by PE (16:0/20: 2);
FIG. 7 is a ROC curve for PE (16:0/20:2) in combination with TAG54:2-FA18:1 for diagnosis of DKDE vs HCs;
FIG. 8 is a ROC curve of the independent diagnosis DKDAvs DKDE of PE (16:0/20: 2);
FIG. 9 is a ROC curve for PE (16:0/20:2) in combination with TAG54:2-FA18:1 for diagnosing DKDAvs DKDE.
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and examples, but not intended to limit the scope of the invention.
Example 1: diagnostic efficacy of serum lipids on diabetes and diabetic nephropathy
First, experimental sample and reagent
169 Healthy Subjects (HCs), 170 type 2 diabetic patients (2-DM), and 238 type 2 diabetic nephropathy patients (124 of them, early DKDE, and 114 of them, late DKDA) were collected in the traditional chinese medicine hospital of jiangsu province. Healthy subjects were healthy, type 2 diabetes complicated with diabetic nephropathy were diagnosed according to the 2015 guide for American diabetes and kidney pathology. The age, sex and body mass index of each group of patients were matched without significant difference. Each group of subjects or patients was randomly divided into training set samples and validation set samples as per table 1.
TABLE 1 training set samples and validation set sample numbers
Figure BDA0002949472990000021
Exclusion criteria: firstly, primary kidney diseases are diagnosed definitely; ② other systemic diseases which can cause lipid urine; ③ acute complications of diabetes and urinary system infection exist in nearly 1 month; fourthly, serious primary diseases such as cardiovascular and cerebrovascular diseases, liver diseases, kidney diseases, hemopoietic systems and the like are combined; fifthly, patients with mental disease unable to cooperate; sixthly, the pregnant or nursing women or the person ready for pregnancy; seventhly, other clinical testers are added in the period of about 1 month; is reluctant to accept the researchers.
The main experimental reagents: methanol, MTBE, isopropanol, acetonitrile, ammonium acetate, ammonium hydroxide.
Second, Experimental methods
1. Serum sample collection and storage
Collecting fasting peripheral blood of a patient in the early morning, placing the fasting peripheral blood in a test tube without anticoagulant, naturally agglutinating for 30-60min at room temperature, after blood coagulation, centrifuging at 2000rpm for 10min, carefully sucking supernatant clear serum liquid into a sterile freeze-drying tube, marking, and storing in a refrigerator at-80 ℃ for later use.
2. UHPLC technology for measuring content level of target lipid in serum
A detection instrument: UHPLC system (Shimazu Nexera X2 LC-30AD, Japan), ESI-Triple quadruple mass spectrometer (SCIEX Triple Quad 5500+, Singapore);
chromatographic conditions are as follows: waters ACQUITY UPLC BEH HILIC (100 mm. times.2.1 mm I.D.,1.7 μm; Waters, Milford, MA, USA). The column temperature was 35 ℃ and the flow rate was 500. mu.l/min. The injection volume was 5. mu.l. The mobile phase consists of two solvents, phase A containing 10mM ammonium acetate (NH)4OAc) water acetonitrile (5:95, v/v) solution, phase B containing 10mM ammonium acetate (NH)4OAc) water acetonitrile (50:50, v/v, ammonium hydroxide pH 8.2). Gradient elution: 0-10.0min, 0.1% -20% B; 10.0-11.0min, 20% -98% B; 11.0-13.0min, 98% B; 13.0-13.1min, 98% -0.1% B; 13.1-16.0min, 0.1% B;
mass spectrum conditions: the mass spectrometer was operated in positive and negative ionization mode with electrospray voltage (capillary voltage) of 4500/-4500V. Typical source conditions are as follows with a curtain gas setting of 35. The ion source temperature was adjusted to 500 ℃. The ion gas source 1(GS1) and the ion gas source 2(GS2) were both set to 50 and 60. The fouling reduction potential was set at 80/-80 volts. In positive and negative modes, the collision exit potential is set to 9/-11 v;
sample treatment: mu.l serum was mixed with 225. mu.l ice methanol. Each sample was then vortexed for 10 seconds and then 750. mu.l of cold MTBE was added, and the mixture was vortexed for 10 seconds and shaken for 10 minutes in a 4 ℃ orbital mixer. Add 188. mu.l of room temperature LC/MS grade water, vortex for 20 seconds, centrifuge at 14000rcf for 2 min. The supernatant was transferred to a clean tube and then evaporated in a SpeedVac sample concentrator for 2h at 45 ℃. The dry oil was dissolved with 100. mu.l of an isopropanol/acetonitrile/water (30:65:5, v/v/v) mixture, the sample vortexed for 10 seconds and then centrifuged at 14000rcf for 10 minutes at 4 ℃.
The content level was represented by the detection peak area of each target lipid in each sample.
3. Data processing method
Training centralization, establishing a regression equation of the single or multiple target serum lipid content levels by using Logistic regression, generating a new variable logit [ P ], carrying out ROC curve analysis on the new variable, and obtaining an optimal cut-off value according to the ROC curve; and (4) in verification set, calculating the diagnosis accuracy of the target serum lipid according to the prediction probability given by SPSS 25.0 software.
Third, experimental results
1. Differences in the levels of target serum lipids in different clinical samples
(1) Differences in the levels of PE (16:0/20:2), TAG54:2-FA18:1 in the serum of 2-DM, HCs
In the training set, the content levels of lipid PE (16:0/20:2) and TAG54:2-FA18:1 in serum of HCs and 2-DM patients are obviously different, as shown in FIG. 1.
(2) Differences in the levels of PE (16:0/20:2), TAG54:2-FA18:1 in DKDDE, HCs sera
In the training set, the content levels of lipid PE (16:0/20:2) and TAG54:2-FA18:1 in serum of HCs and DKDE patients are obviously different, as shown in FIG. 2.
(3) Differences in the levels of PE (16:0/20:2), TAG54:2-FA18:1 in DKDA, DKDE sera
In the training set, the content levels of lipid PE (16:0/20:2) and TAG54:2-FA18:1 in the serum of patients with DKDA and DKDE are obviously different, as shown in figure 3.
2. Diagnostic discrimination efficacy of target serum lipids for different clinical samples
2.1 training set construction of logistic regression equation
(1) PE (16:0/20:2) independent diagnosis of 2-DM vs HCs
In the training set, the content level of serum lipid PE (16:0/20:2) of each sample is used as an independent variable, and the group (2-DM, HCs) is used as a dependent variable, so as to construct a regression equation logit [ p ] ═ 0.281+2.174X1, wherein: x1 is the PE (16:0/20:2) content level.
(2) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for diagnosis of 2-DM vs HCs
In the training set, the content levels of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are used as independent variables, and groups (2-DM and HCs) are used as dependent variables, so that a regression equation logit [ p ] ═ 0.450+2.275X1+1.828X2 is constructed, wherein: x1 is PE (16:0/20:2) content level, and X2 is TAG54:2-FA18:1 content level.
(3) Independent diagnosis of DKDDE vs HCs by PE (16:0/20:2)
In the training set, the level of serum lipid PE (16:0/20:2) content of each sample is used as an independent variable, and the group (DKDE, HCs) is used as a dependent variable, so as to construct a regression equation logit [ p ] ═ 0.347+2.533X1, wherein: x1 is the PE (16:0/20:2) content level.
(4) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for DKDE vs HCs diagnosis
In the training set, the content levels of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are used as independent variables, and the group (DKDE and HCs) are used as dependent variables, so that a regression equation, namely logic [ p ] ═ 0.233+3.247X1+3.206X2, is constructed, wherein: x1 is PE (16:0/20:2) content level, and X2 is TAG54:2-FA18:1 content level.
(5) Independent diagnosis of DKDAvs DKDDE by PE (16:0/20:2)
In the training set, the content level of serum lipid PE (16:0/20:2) of each sample is used as an independent variable, and the group (DKDA, DKDE) is used as a dependent variable, so as to construct a regression equation logit [ p ] ═ 0.654+1.715X1, wherein: x1 is the PE (16:0/20:2) content level.
(6) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for DKDavs DKDDE diagnosis
In the training set, the content levels of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are used as independent variables, and the group (DKDA and DKDE) is used as dependent variables, so that a regression equation logit [ p ] ═ 0.654+1.715X1 is constructed, wherein: x1 is the PE (16:0/20:2) content level.
2.2 training set determination of optimal discrimination thresholds
(1) PE (16:0/20:2) independent diagnosis of 2-DM vs HCs
In the training set, the content level of the serum lipid PE (16:0/20:2) of each sample is substituted into the regression equation to obtain the regression value logit [ p ] of each sample in the training set, the possible regression value is used as a diagnosis point, the sensitivity and the specificity are calculated, and accordingly, an ROC curve (shown in figure 4) is drawn, the AUC can reach 0.753, and the accuracy is high. The optimal cut-off value of 0.549 for diagnosing and distinguishing 2-DM vs HCs is obtained according to the ROC curve.
(2) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for diagnosis of 2-DM vs HCs
In the training set, the content levels of the serum lipid PE (16:0/20:2) and the TAG54:2-FA18:1 of each sample are substituted into the regression equation to obtain the regression value logit [ p ] of each sample in the training set, the possible regression value is used as a diagnosis point, the sensitivity and the specificity are calculated, and accordingly, an ROC curve (shown in figure 5) is drawn, the AUC can reach 0.803, and the accuracy is high. The optimal cut-off value of 0.457 for diagnosing and distinguishing 2-DM vs HCs is obtained according to the ROC curve.
(3) Independent diagnosis of DKDDE vs HCs by PE (16:0/20:2)
In the training set, the content level of the serum lipid PE (16:0/20:2) of each sample is substituted into the regression equation to obtain the regression value logit [ p ] of each sample in the training set, the possible regression value is used as a diagnosis point, the sensitivity and the specificity are calculated, and accordingly, an ROC curve (shown in figure 6) is drawn, the AUC can reach 0.790, and the accuracy is high. The optimal cut-off value of 0.386 for diagnosing and distinguishing DKDDE vs HCs is obtained according to the ROC curve.
(4) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for DKDE vs HCs diagnosis
In the training set, the content levels of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are substituted into the regression equation to obtain the regression value logit [ p ] of each sample in the training set, the possible regression value is used as a diagnosis point, the sensitivity and the specificity are calculated, and accordingly, an ROC curve (shown in figure 7) is drawn, the AUC can reach 0.878, and the accuracy is high. The optimal cut-off value of 0.314 for diagnostic differentiation of DKDE vs HCs was obtained from the ROC curve.
(5) Independent diagnosis of DKDAvs DKDDE by PE (16:0/20:2)
In the training set, the content level of the serum lipid PE (16:0/20:2) of each sample is substituted into the regression equation to obtain the regression value logit [ p ] of each sample in the training set, the possible regression value is used as a diagnosis point, the sensitivity and the specificity are calculated, and accordingly, an ROC curve (shown in figure 8) is drawn, the AUC can reach 0.698, and the accuracy is high. The optimal cut-off value for the diagnostic DKDavDKDE was 0.480 according to the ROC curve.
(6) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for DKDavs DKDDE diagnosis
In the training set, the content levels of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are substituted into the regression equation to obtain the regression value logit [ p ] of each sample in the training set, the possible regression value is used as a diagnosis point, the sensitivity and the specificity are calculated, and accordingly, an ROC curve (shown in figure 9) is drawn, the AUC can reach 0.698, and the accuracy is high. The optimal cut-off value of 0.480 for diagnosing and distinguishing DKDavs DKDDE is obtained according to the ROC curve.
2.3 validation set validation diagnostic accuracy
(1) PE (16:0/20:2) independent diagnosis of 2-DM vs HCs
In the validation set, the data of the serum lipid PE (16:0/20:2) content level of each sample is introduced into SPSS 25.0 software, so that the regression value logit [ p ] of each sample in the validation set can be obtained, and the prediction probability is obtained, and the accuracy rate of distinguishing 2-DM vs HCs by the target lipid is 73.9 percent (88/119) by dividing the number of correct predicted samples by the total number of samples.
(2) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for diagnosis of 2-DM vs HCs
In the verification set, the data of the content level of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are introduced into SPSS 25.0 software, so that a regression value logit [ p ] of each sample in the verification set can be obtained, the prediction probability is obtained, and the accuracy rate of distinguishing 2-DM vs HCs by the target lipid is 73.9 percent (88/119) by dividing the number of correct predicted samples by the total number of samples.
(3) Independent diagnosis of DKDDE vs HCs by PE (16:0/20:2)
In the validation set, the data of the content level of the serum lipid PE (16:0/20:2) of each sample is imported into SPSS 25.0 software, so that the regression value logit [ p ] of each sample in the validation set can be obtained, and the prediction probability is obtained, and the accuracy of the DKDE vs HCs of the target lipid differentiation is 75.6 percent (93/123) by dividing the number of correct predicted samples by the total number of samples.
(4) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for DKDE vs HCs diagnosis
In the verification set, the data of the content level of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are introduced into SPSS 25.0 software, so that a regression value logit [ p ] of each sample in the verification set can be obtained, the prediction probability is obtained, and the accuracy of the DKDE vs HCs is 84.6 percent (104/123) by dividing the number of correct predicted samples by the total number of samples.
(5) Independent diagnosis of DKDAvs DKDDE for PE (16:0/20:2)
In the validation set, the data of the serum lipid PE (16:0/20:2) content level of each sample is introduced into SPSS 25.0 software, so that the regression value logit [ p ] of each sample in the validation set can be obtained, and the prediction probability is obtained, and the accuracy of the DKDAvs DKDDE is 75% (96/128) by dividing the number of correct predicted samples by the total number of samples.
(6) PE (16:0/20:2) in combination with TAG54:2-FA18:1 for the diagnosis of DKDAvs DKDDE
In the verification set, the data of the content level of serum lipid PE (16:0/20:2) and TAG54:2-FA18:1 of each sample are introduced into SPSS 25.0 software, so that a regression value logit [ p ] of each sample in the verification set can be obtained, the prediction probability is obtained, and the accuracy of DKDAvs DKDODE is 78.9 percent (101/128) by dividing the number of correct predicted samples by the total number of samples.
Example 2: diagnostic kit
A kit for diagnosing type 2 diabetes, comprising a detection reagent for detecting serum lipid PE (16:0/20:2), or comprising a detection reagent for detecting serum lipid PE (16:0/20:2) and TAG54:2-FA18: 1.
A kit for diagnosing early diabetic nephropathy comprises a detection reagent for detecting serum lipid PE (16:0/20:2) or a detection reagent for detecting serum lipid PE (16:0/20:2) and TAG54:2-FA18: 1.
A kit for diagnosing and distinguishing early stage and late stage of diabetic nephropathy comprises a detection reagent for detecting serum lipid PE (16:0/20:2) or a detection reagent for detecting serum lipid PE (16:0/20:2) and TAG54:2-FA18: 1.
The above-described embodiments are intended to be illustrative of the nature of the invention, but those skilled in the art will recognize that the scope of the invention is not limited to the specific embodiments.

Claims (3)

1. Application of a detection reagent for detecting serum PE (16:0/20:2) and a detection reagent for detecting serum TAG54:2-FA18:1 in preparing a kit for diagnosing type 2 diabetes.
2. The application of a detection reagent for detecting serum PE (16:0/20:2) and a detection reagent for detecting serum TAG54:2-FA18:1 in preparing a kit for diagnosing early diabetic nephropathy, wherein the diabetic nephropathy is type 2 diabetes mellitus complicated diabetic nephropathy.
3. Application of a detection reagent for detecting serum PE (16:0/20:2) in preparation of a kit for diagnosing and distinguishing early and late diabetic nephropathy, wherein the diabetic nephropathy is type 2 diabetes mellitus complicated by diabetic nephropathy.
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