CN114965787A - Detection reagent and kit for predicting delivery time - Google Patents
Detection reagent and kit for predicting delivery time Download PDFInfo
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- CN114965787A CN114965787A CN202210643233.8A CN202210643233A CN114965787A CN 114965787 A CN114965787 A CN 114965787A CN 202210643233 A CN202210643233 A CN 202210643233A CN 114965787 A CN114965787 A CN 114965787A
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Abstract
The invention discloses a detection reagent and a kit for predicting delivery time, which can predict the delivery time through urine metabolite characteristics, so that medical personnel can take corresponding measures according to a prediction result to reduce the risk of premature mothers and acquired newborn baby, and the adopted kit can adopt urine of a subject as a detection sample to eliminate discomfort of the subject during blood sampling.
Description
Technical Field
The invention relates to the technical field of personalized medicine, in particular to a detection reagent and a kit for predicting delivery time.
Background
Parturition, which refers to emerging from the mother as a new individual; in particular to the period and process of the fetus departing from the mother as an individual existing alone. The whole process of parturition is divided into 3 stages, also called 3 stages. The first stage of labor, the ostial dilatation phase. The second phase of labor, fetal delivery. The third stage of labor, the term "placental expulsion," refers to the process from fetal expulsion to placental expulsion. Preterm labor and labor are the major causes of perinatal morbidity and mortality, and even though numerous risk factors associated with preterm labor have been identified, whether labor is imminent based on the proximity to the term, based on the contraction of the body's uterus, the time of labor is not currently accurately predicted.
Although there is currently an increased understanding of the biology of the metabolic system of normal, term and abnormal pregnancy, there is no metabolic-based prediction of pregnancy labor time in clinical practice.
Disclosure of Invention
The purpose of the present invention is to provide a detection reagent and a kit for predicting the labor time in pregnancy, which can predict the labor time in pregnancy.
To achieve the above objects, the present invention provides a detection reagent for predicting labor time, comprising urine metabolite features having mass-to-charge ratios as follows: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
The invention also provides the use of urine metabolite features in the manufacture of a kit for predicting time of labour, the urine metabolite features comprising the following mass to charge ratios: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
The invention also provides a kit for predicting labor time, which comprises a detection reagent with urine metabolite characteristics, wherein the urine metabolite characteristics comprise the following mass-to-charge ratios: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
Optionally, the test sample of the kit for predicting labor time is urine of the subject at 14 to 36 weeks of pregnancy.
Compared with the prior art, the invention has the following beneficial effects:
according to the technical scheme, the delivery time can be predicted through the urine metabolite characteristics, so that medical personnel can take corresponding measures according to the prediction result, and the risks of a premature mother and a late-day newborn disease infant are reduced.
According to the technical scheme, when the delivery time is predicted, the adopted kit can adopt the urine of the subject as a detection sample, so that the discomfort of the subject during blood sample collection is eliminated.
Drawings
Fig. 1 is a graph showing prediction of labor time in the S area and the a area.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a detection reagent for predicting delivery time, which comprises the following characteristics of urine metabolites with the mass-to-charge ratios: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
The urine metabolite features in the detection reagent of the embodiment of the invention can be applied to the preparation of a kit for predicting the labor time. The kit for predicting the labor time comprises a detection reagent with urine metabolite characteristics, wherein the urine metabolite characteristics comprise the following mass-to-charge ratios: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
When the kit for predicting the labor time is used for detection, the detection sample is urine of a pregnant subject, in particular urine of the pregnant subject at 14 to 36 weeks of pregnancy. Compared with other biological matrixes, the urine sample is used as a detection sample, is easy and noninvasive in collection, and eliminates the discomfort of a subject in blood sample collection.
The prediction of labor time by urine metabolite profiles is described in detail below.
1) Sample collection
Subjects include women between 18 and 45 years of age at birth, with the subject either giving birth at term (37 weeks or later) or preterm (giving birth earlier than 37 weeks). Urine samples were taken from subjects once a week during the first, second and third months of pregnancy. Subjects were from two places: area S (19 normal patients and 23 premature patients) and area a (12 normal patients and 14 premature patients). Wherein 19 normal subjects in the S region had 482 urine samples in total, 23 preterm patients had 439 urine samples in total; a total of 208 urine samples from 12 normal subjects in area a, and 138 urine samples from 14 preterm patients.
2) MS analysis
Urine samples from full and preterm subjects, as well as Quality Control (QC) samples, were removed from the-80 ℃ freezer and thawed on ice. For 50 muL urine, 10 muL recovery standard working solution (100 muM 2D 5-heptanoic acid, 100 muM 13C5, 15N-proline in methanol) is added. And (3) carrying out metabolite extraction by using 50 muL precooled extraction buffer (100 muM 2D 4-taurine, 100 muM 13C 6-arginine, 100 muM 2D 8-phenylalanine and 10 muM 2D9-DHEAS methanol solution as instrument standards). The sample was vortexed vigorously for 1 minute. After centrifugation at 12000g for 10 minutes at 4 ℃, 90 μ L of supernatant was collected and stored at-20 ℃.
Mass spectrometry was performed by using a Vanqish UPLC system with a ZICHILIC column (2.1mmx, 100mmx, 3.5 μm) and a Q active plus mass spectrometer (Thermo Scientific, San Jose, Calif.). 10 μ L of urine extract was injected into the UPLC system, where the gradient mobile phase contained 10mM ammonium acetate in water (solution A) and 10mM ammonium acetate in acetonitrile (solution B), and the parameter settings for the Vanqish UPLC system are shown in Table 1.
TABLE 1 parameter settings for Vanqish UPLC System
Samples were run through a 25 ℃ ZICHILIC column (2.1mmx, 100mmx, 3.5 μm) connected to a Security Guard Ultracard-UPLC C18 column (phenomenex, Torrance, CA, US) and injected in a positive and negative ESI mode.
The parameters of the Q active plus mass spectrometer were set as follows: 1 micro-scan in Full MS mode with a scan range of 70 to 1000m/z, a resolution of 70000FWMH, a spray voltage of 4kV, sheath gas (N) 2 ) Flow rate of 30L/min, assist gas (N) 2 ) The flow rate was 10L/min, the capillary temperature was 325 deg.C, the S-lens RF level was 55, and the auxiliary gas heater temperature was 325 deg.C.
3) Course and result of the study
Firstly, carrying out univariate analysis on the metabolite features, and screening out the metabolite features with obvious strong correlation with the delivery time according to the p value of less than 0.05 and the absolute value | r | of the Pearson correlation coefficient of more than 0.3. Wherein r is a Pearson correlation coefficient, is a relative index for measuring the correlation direction and the correlation degree between two variables, and the value is within +/-1, and the closer to +/-1, the stronger the correlation between the two variables is; when r is 0, it means that there is no correlation between the two variables. In the univariate analysis of the examples of the present application, the pearson correlation coefficient r represents the correlation between the time of labor in a term labor sample and the abundance value of the metabolite feature.
And then constructing a linear model by using the characteristics of the screened remarkably strongly related metabolites through an elastic network regression algorithm in an R language package Glmnet. Glmnet is a package that fits generalized linear and similar models by penalizing maximum likelihood. The elastic network regression algorithm belongs to a conventional algorithm, is a mixed technology of ridge regression and lasso regression, and uses regularization of both L1 and L2, lasso regression may randomly select one of the two, and elastic regression may both select, so the elastic network regression has the advantage of encouraging the population effect in the case of highly correlated variables.
Finally, 19 metabolite features were determined from elastic network regression models for predicting time to delivery with mass to charge ratios m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272, respectively. Table 2 shows Pearson's correlation coefficient and the algorithm coefficient of the elastic network regression model for the 19 metabolite features m/z.
The equation for calculating the delivery time DT from the elastic network regression model is as follows: DT = 0.3892 + 0.7350 × m/z 114.934 abundance value + 2.8181 × m/z 2.8181 abundance value + 2.8181 m/z 2.8181 abundance value-2.8181 × m/z 2.8181 abundance value-2.8181 × m/z 2.8181 abundance value-36030.033661 × m/z 2.8181 abundance value 533.189 abundance + 5.5458 m/z 534.193 abundance-0.1315 m/z 572.272 abundance. Where 0.3892 is the intercept of the linear equation.
The abundance value is a response measurement of a liquid chromatography-mass spectrometry detection instrument on detection of a detected object, and belongs to a general concept in the field of mass spectrometry. The abundance value was obtained by processing with the XCMS software package according to the following procedure: firstly, extracting liquid chromatogram-mass spectrum combined data from an mzXML file; then, mass spectrum peaks are aligned and grouped on the m/z spectrum, and retention time is synchronous on a chromatogram; finally, the alignment of the peak groups is updated after retention time correction, and the aligned groups are normalized using the statTarget package by applying local estimation scatter plot smoothing (LOESS) in different experimental batches, thereby obtaining abundance values of metabolite features.
TABLE 2 Pearson correlation coefficient r and algorithm coefficient corresponding to the differential expression feature m/z
FIG. 1 shows the prediction of labor time in the areas S and A (■ represents the sample for the subject in the area S; tangle-solidup represents the sample for the subject in the area A). As can be seen from FIG. 1, the predicted R for term in the S region and the A region 2 0.87 and 0.57, respectively, and a RMSE of 3.06 and 4.49, respectively. Predicted R for preterm birth in S and A regions 2 0.76 and 0.56, respectively, and 5.40 and 6.60, respectively. R 2 And Root Mean Square Error (RMSE) is a commonly used evaluation index in regression analysis predictive models, where R is 2 I.e., the decision coefficient, is a statistic for measuring goodness of fit. R 2 The larger the interpretation of the dependent variable by the independent variable, the higher the percentage of total variation by the variation caused by the independent variable, and the denser the observation points are near the regression line. The decision coefficient only describes the joint influence degree of all the explanatory variables listed in the model on the dependent variable, but does not describe the influence degree of a single explanatory variable in the model. R 2 The closer to 1, the better the fitting. RMSE, root mean square error, is the square root of the ratio of the square of the deviation of the predicted value from the true value to the number of observations, n, and is a measure of the deviation between the observed value and the true value. The root mean square error is used for explaining the dispersion degree of the samples, and the smaller the root mean square error is, the better the model prediction capability is.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A test agent for predicting labor time comprising a urine metabolite profile having mass to charge ratios as follows: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
2. Use of urine metabolite features in the manufacture of a kit for predicting labor time, wherein the urine metabolite features comprise the following mass to charge ratios: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
3. A kit for predicting labor time comprising a detection reagent having a urine metabolite profile comprising the following mass to charge ratios: m/z 114.934, m/z 148.122, m/z 164.116, m/z 220.119, m/z 231.941, m/z 267.110, m/z 273.092, m/z 275.160, m/z 280.154, m/z 298.164, m/z 365.173, m/z 412.189, m/z 429.196, m/z 445.189, m/z 464.201, m/z 493.281, m/z 533.189, m/z 534.193, m/z 572.272.
4. The kit for predicting labor time of claim 3, wherein the test sample of the kit for predicting labor time is urine of the subject at 14 to 36 weeks of gestation.
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