CN114636774B - Biological metabolism marker composition for predicting adolescent hypertension disease risk - Google Patents
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
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Abstract
The present invention relates to a biomarker composition for predicting the risk of developing juvenile hypertension, which is selected from two or more of hypoxanthine, miglitol, homoccitrulline, topiramate, thiocholic acid glycine, imidazol-5-yl-pyruvate, p-isopropylaldehyde, L-proline, β -picelline, and 1-naphthylamine. The invention uses a non-targeted urine metabonomics analysis method to compare the metabolites differentially expressed between teenager hypertension patients and normotensive teenagers, screens potential biological metabolic markers, and has stronger clinical guidance significance for early diagnosis of teenager hypertension.
Description
Technical Field
The invention relates to the field of clinical medicine, in particular to a biological metabolism marker for predicting the risk of teenager hypertension.
Background
At present, hypertensive disorders, which present with a continuously growing case and a younger trend, present a serious disease burden for adolescents. The occurrence of adolescent hypertension is closely related to future target organ damage (e.g., coronary calcification, ventricular hypertrophy, and increased layer thickness in carotid intima), which is not only the strongest predictor of adult hypertension, but also is prone to premature death. At present, the scholars at home and abroad agree that the earlier the hypertension is, the longer the treatment period is, the greater the control difficulty is, and the worse the prognosis is. Therefore, the method has important public health and clinical significance for protecting the health of teenagers and preventing adult hypertension by identifying and intervening the teenager hypertension as early as possible.
Teenagers differ from adults in their diagnostic criteria for their specificity in the period of growth. The current judgment of the rise of the blood pressure of the children and the teenagers at home and abroad is mainly based on three dimensions of gender, age and height, and relates to a plurality of boundary values. For example, the standard of China (WS/T610-2018) judges that the limit value exceeds 700, and the judging method is not easy to be rapidly mastered by basic medical institutions and school personnel. In addition, juvenile hypertension is often asymptomatic, and current attention is not paid enough, resulting in early detection of the hypertension. For the above reasons, the pathophysiological characteristics of juvenile hypertension have not yet been fully elucidated.
Metabonomics is a technique that allows for high throughput quantitative detection and analysis of all endogenous small molecule metabolites (molecular weight <1000 Da) in an organism. Metabonomics amplifies the tiny changes of gene and protein expression, more easily reflects the influence of internal and external factor changes on system metabolism, and helps to reveal the relationship between small molecules and physiological and pathological effects.
At present, the report of researching the onset of teenager hypertension by adopting metabonomics at home and abroad is not very common, and an effective diagnosis method with higher prediction capability is lacking. Therefore, the research on the pathogenesis of juvenile hypertension based on metabonomics has important theoretical and practical significance by taking the screened potential biological metabolic markers as prediction targets.
Disclosure of Invention
In order to solve the technical problems, the invention comprises the following aspects:
a first aspect of the present invention provides a biomarker composition for predicting the risk of developing juvenile hypertension, the biomarker composition being selected from two or more of (1) hypoxanthine, (2) miglitol, (3) homocysteine, (4) topiramate, (5) thiocholic acid glycine, (6) imidazol-5-yl-pyruvate, (7) p-isopropylaldehyde, (8) L-proline, (9) β -bissolanesene and (10) 1-naphthylamine.
Preferably, the biomarker composition is selected from three, four, five, six, seven, eight, nine or ten of the above components (1) - (10).
More preferably, the biomarker composition is selected from four or more of the above-described components (1) to (10).
Further preferably, the biomarker composition is selected from six or more of the above-mentioned components (1) to (10).
Further preferably, the biomarker composition is selected from eight or more of the above-described components (1) to (10).
Most preferably, the biomarker composition is a composition of ingredients (1) - (10) above.
Preferably, the biomarker composition is selected from two, three, four, five, six, seven or eight of the above ingredients (1) - (8).
More preferably, the biomarker composition is a composition of the above components (1) - (8).
Preferably, the biomarker composition is selected from two, three, four or five of the above components (1) - (5).
More preferably, the biomarker composition is a composition of the above components (1) - (5).
Preferably, the biomarker composition is selected from the group consisting of the compositions of components (1) and (2) above.
In a second aspect the invention provides a kit for predicting the risk of developing juvenile hypertension, the kit comprising reagents for predicting a biomarker composition selected from two or more of (1) hypoxanthine, (2) miglitol, (3) homoccitrulline, (4) topiramate, (5) thiocholic glycine, (6) imidazol-5-yl-pyruvate, (7) p-isopropylaniline, (8) L-proline, (9) β -picelline, and (10) 1-naphthylamine, and instructions for use.
Preferably, the biomarker composition is selected from three, four, five, six, seven, eight, nine or ten of the above components (1) - (10).
More preferably, the biomarker composition is selected from four or more of the above-described components (1) to (10).
Further preferably, the biomarker composition is selected from six or more of the above-mentioned components (1) to (10).
Further preferably, the biomarker composition is selected from eight or more of the above-described components (1) to (10).
Most preferably, the biomarker composition is a composition of ingredients (1) - (10) above.
Preferably, the biomarker composition is selected from two, three, four, five, six, seven or eight of the above ingredients (1) - (8).
More preferably, the biomarker composition is a composition of the above components (1) - (8).
Preferably, the biomarker composition is selected from two, three, four or five of the above components (1) - (5).
More preferably, the biomarker composition is a composition of the above components (1) - (5).
Preferably, the biomarker composition is selected from the group consisting of the compositions of components (1) and (2) above.
In a third aspect the invention provides the use of a biomarker composition selected from two or more of (1) hypoxanthine, (2) miglitol, (3) homoccitrulline, (4) tropate, (5) thiocholic acid glycine, (6) imidazol-5-yl-pyruvate, (7) p-isopropylaldehyde, (8) L-proline, (9) β -pillar-oil alkene and (10) 1-naphthylamine in the manufacture of a kit for predicting the risk of developing juvenile hypertension.
Preferably, the biomarker composition is selected from three, four, five, six, seven, eight, nine or ten of the above components (1) - (10).
More preferably, the biomarker composition is selected from four or more of the above-described components (1) to (10).
Further preferably, the biomarker composition is selected from six or more of the above-mentioned components (1) to (10).
Further preferably, the biomarker composition is selected from eight or more of the above-described components (1) to (10).
Most preferably, the biomarker composition is a composition of ingredients (1) - (10) above.
Preferably, the biomarker composition is selected from two, three, four, five, six, seven or eight of the above ingredients (1) - (8).
More preferably, the biomarker composition is a composition of the above components (1) - (8).
Preferably, the biomarker composition is selected from two, three, four or five of the above components (1) - (5).
More preferably, the biomarker composition is a composition of the above components (1) - (5).
Preferably, the biomarker composition is selected from the group consisting of the compositions of components (1) and (2) above.
The invention has the technical effects that:
According to the invention, through large sample data, the non-targeted urine metabonomics analysis method is utilized to compare the metabolites differentially expressed between teenager hypertension patients and normotensive teenagers, potential biological metabolic markers are screened, ten biological metabolic markers with different expression are used as markers for distinguishing whether hypertension exists, and the method has strong clinical guidance significance for early diagnosis of teenager hypertension.
Drawings
FIG. 1 is a graph showing the positive ion mode OPLS-DA score of test example 1 of the invention;
FIG. 2 is a graph of the positive ion mode OPLS-DA loading of test example 1 of the invention;
FIG. 3 is a diagram of the positive ion mode OPLS-DA S-plot of test example 1 of the invention;
FIG. 4 is a diagram showing the positive ion mode OPLS-DA substitution test of test example 1 of the invention;
FIG. 5 is a graph showing the negative ion mode OPLS-DA score of test example 1 of the invention;
FIG. 6 is a graph showing the negative ion mode OPLS-DA loading of test example 1 of the invention;
FIG. 7 is a diagram of the negative ion mode OPLS-DA S-plot of test example 1 of the invention;
FIG. 8 is a diagram showing the negative ion mode OPLS-DA substitution test of test example 1 of the invention;
FIG. 9 is a volcanic chart of differential metabolites of test example 1 of the present invention;
FIG. 10 is a graph showing the ROC profile of the differential metabolite of test example 1 of the present invention.
Detailed Description
Test example 1 screening of teenager hypertension biological Metabolic markers
Early-stage eto-su-city children and teenagers health promotion program (Health Promotion Program for CHILDREN AND Adolescents, HPPCA) with 824 teenagers of 12-17 years old were subjected to non-same day three-time blood pressure measurement, and morning urine collection and physical examination were partially completed. 51 teenager hypertension cases are randomly selected, the sex, the age and the height are matched according to the ratio of 1:1, and the non-targeted metabonomics qualitative detection of morning urine is completed by applying the UPLC-QTOF/MS technology.
The original mass spectrometry offmachine file was converted to mzXML file format by means of MSConvert tool in Proteowizard software package (v3.0.8789). Peak detection, peak filtration, peak alignment were performed using R XCMS software package to obtain quantitative lists of substances, parameters set by bw=2, ppm=15, peak width=c (5, 30), mzwid =0.015, mzdiff=0.01, method= 'centWave'. The identification of the substances was performed using public database HMDB, massbank, lipidMaps, mzclound, KEGG and a self-built substance library, with parameters set to ppm <30ppm. The LOESS signal correction method based on QC samples realizes data correction and eliminates system errors. The quality control of the data filters out >30% of the RSD in the QC samples.
Carrying out orthogonal partial least squares discriminant analysis (OPLS-DA) dimension reduction analysis on sample data by adopting an R software package Ropls, carrying out scaling treatment on the data, and respectively drawing a score graph (positive and negative ion modes are shown in figures 1 and 5), a load graph (positive and negative ion modes are shown in figures 2 and 6), a Splot graph (positive and negative ion modes are shown in figures 3 and 7) and a replacement check graph (positive and negative ion modes are shown in figures 4 and 8), so as to show the difference of metabolite compositions among the samples.
Wherein the abscissa in fig. 1 and 5 represents the first principal component interpretation degree, and the ordinate represents the second principal component interpretation degree. Dots represent experimental samples and colors represent different groupings. The more aggregated the intra-group samples, the more dispersed the inter-group samples, indicating that the results are more reliable. The results in the positive and negative ion modes shown in fig. 1 and 5 show that the samples in the groups are more aggregated, and the samples between the groups are dispersed, thus indicating that the test results are reliable.
The abscissa in the load figures 2 and 6 represents the first principal component interpretation, and the ordinate represents the second principal component interpretation. Dots represent metabolic molecules. The relation between each variable and the principal component is displayed, the effect is to observe the interpretation contribution degree of each variable to the original variable, and the first 10 molecules with larger contribution degree are displayed in default. Figures 2 and 6 show the molecular numbering of the first 10 contributions in positive and negative ion modes, respectively.
Splot the abscissa in FIGS. 3 and 7 represents the co-correlation coefficient of the main component with the metabolite, and the ordinate represents the correlation coefficient of the main component with the metabolite. The S-plot is generally used to select metabolites that are strongly related to the main component of the biological process, but on the other hand metabolites that are strongly related to Y may also be selected. The closer to both corners the more important the metabolite. The top right hand corner and bottom left hand corner, respectively, are shown by default for the first 10 molecule names. FIGS. 3 and 7 show metabolites that are selected in positive and negative ion modes and have a relatively strong correlation with the main component of the biological process and metabolites that have a strong correlation with Y.
The result is described as reliable and valid when the replacement test pattern satisfies any of the following points: 1. all blue Q2 points are lower than the original blue Q2 point at the rightmost side from left to right (the rightmost blue Q2 point and the green R2 point in the figure are coincident at the upper right corner); 2. the regression line of the points intersects the abscissa or is less than 0. The results in FIGS. 4 and 8 show that OPLS-DA results are reliable and efficient.
Calculating the P value according to the statistical test, calculating the variable projection importance (VIP) according to the OPLS-DA dimension reduction method, calculating the difference multiple between groups according to the fold change, and measuring the influence intensity and interpretation ability of the content of each metabolite component on sample classification and discrimination to assist in screening the marker metabolites. Metabolite molecules are considered statistically significant when P values <0.05 and VIP > 1. A total of 322 differential metabolites were found, 256 up-regulated, 66 down-regulated (VIP >1, p value < 0.05) and volcanic images were plotted (Volcano Plot, see fig. 9).
Each point in the volcanic plot represents a metabolite and the abscissa represents Log2 Log of the quantitative fold difference of a metabolite in two samples; the ordinate represents the log10 of the P value. The larger the absolute value of the abscissa is, the larger the multiple difference of the expression quantity of a certain metabolite between two samples is; the larger the ordinate value, the more remarkable the differential expression, and the more reliable the differential expression metabolite obtained by screening. In the figure, red dots represent up-regulated differentially expressed metabolites, blue dots represent down-regulated differentially expressed metabolites, and grey dots represent detected but unsatisfied metabolites screened by the filtration parameters. The top 5 metabolite names with the smallest P-value are shown by default.
The volcanic chart can intuitively show the distribution situation of the differential metabolites of two groups of samples, and the first 5 up-regulated differential expression metabolites and the first 5 down-regulated differential expression metabolites with the minimum P value are screened out from the volcanic chart, and the results are shown in the following table:
by using a Logistic regression model, a corresponding ROC curve is drawn, and the 10 different metabolites are found to have good identification capability on teenager hypertension, wherein AUC values of all metabolites are respectively more than 0.8 when being diagnosed independently, specific AUC values of the metabolites are shown in figure 10 (the ROC curve result of figure 10, wherein the abbreviations of the respective AUC values are shown as ,Hyp：Hypoxanthine;Tro：Tropate;Mig：Miglitol;Hom：Homocitrulline;Imi：Imidazol-5-yl-pyruvate;Sul：Sulfolithocholylglycine;Pro：L-Proline;Cum：Cuminaldehyde;bet：beta-Cubebene;Nap：1-Naphthylamine). in figure 10, and the 10 different metabolites have good identification capability on teenager hypertension).
Although specific embodiments of the invention have been described, those skilled in the art will recognize that many changes and modifications may be made thereto without departing from the scope or spirit of the invention. Accordingly, the present invention is intended to embrace all such alterations and modifications that fall within the scope of the appended claims and equivalents thereof.
Claims (3)
1. A biomarker composition for predicting the risk of developing juvenile hypertension, wherein the biomarker composition is selected from three, four, five, six, seven, eight, nine or ten of (1) hypoxanthine, (2) miglitol, (3) homocysteine, (4) topiramate, (5) thiocholic acid glycine, (6) imidazol-5-yl-pyruvate, (7) p-isopropylaldehyde, (8) L-proline, (9) β -picelline, and (10) 1-naphthylamine.
2. A kit for predicting the risk of developing juvenile hypertension, comprising reagents for predicting a biomarker composition selected from three, four, five, six, seven, eight, nine or ten of (1) hypoxanthine, (2) miglitol, (3) homoccitrulline, (4) topiramate, (5) thiocholate glycine, (6) imidazol-5-yl-pyruvate, (7) p-isopropylaldehyde, (8) L-proline, (9) β -picelline, and (10) 1-naphthylamine, and instructions for use.
3. Use of a biomarker composition for the preparation of a kit for predicting the risk of developing juvenile hypertension, wherein the biomarker composition is selected from three, four, five, six, seven, eight, nine or ten of (1) hypoxanthine, (2) miglitol, (3) homocysteine, (4) tropate, (5) thiocholic acid glycine, (6) imidazol-5-yl-pyruvate, (7) p-isopropylaldehyde, (8) L-proline, (9) β -pillar-oil alkene, and (10) 1-naphthylamine.
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