CN117079822B - System for predicting fetal brain injury by four factors in early and middle gestation period - Google Patents
System for predicting fetal brain injury by four factors in early and middle gestation period Download PDFInfo
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Abstract
The invention relates to a system and a method for predicting fetal brain injury by four factors in early and middle gestation, wherein the system comprises a detection sample acquisition unit, a detection sample injection unit, a concentration acquisition unit of a target metabolite, a data analysis and risk prediction model construction unit and a judgment standard setting unit; based on the four metabolites identified with significant inter-group differences, performing a two-term logistic regression analysis on the log2 transformed values of the absolute concentrations thereof to calculate the probability of brain injury incidence of the twin gestation fetuses; taking the absolute concentration of four metabolites as an independent variable X, the logistic regression probability function is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein: x= [ X1, X2, X3, X4]Is an independent variable matrix; z=wx+w0; w= [ W1, W2, W3, W4]]The weight coefficient matrix corresponding to X is obtained; w0 is a constant term; and setting an optimal judgment threshold, and judging that fetal brain damage occurs if the probability is larger than the optimal judgment threshold.
Description
Technical Field
The invention relates to the technical field of medical detection, in particular to a system and a method for predicting fetal brain injury by four factors in early and middle gestation.
Background
Since the 80 s of the 20 th century, the global twin rate increased by one third, with 9.1 to 12.0 twin per 1000 newborns, reaching about 160 ten thousand twin per year. Wherein, single chorion double amniotic Membrane (MCDA) twin is a complex twin pregnancy accounting for 30% of MC twin. The presence of the shared placenta and anastomosed vessel often results in serious pregnancy complications such as selective fetal growth restriction (sFGR), double transfusion syndrome (TTTS), etc., significantly increasing the risk of perinatal mortality and distant poor neurological outcomes in newborns, and presenting additional challenges to obstetrician clinical management.
The central nervous system is one of the organs of the fetus that starts to develop at the earliest in utero, and the growth rate of the human central nervous system is far higher than any other organ system from 4 weeks after conception to 3 years after birth. Fetal brain prenatal development is a highly coordinated spatiotemporal process, and the central nervous system is also more susceptible to interference from adverse environments than other organs due to its long-term, dynamic development. Development of the fetal central nervous system begins with cell proliferation and neurogenesis 8-16 days after conception. At this time, the neuroepithelial cells of the ectoderm form a neural tube, producing neural progenitor cells and neurons. The neuroepithelial cells regenerate stem cell regions of the ventricular zone and generate neurons by symmetrical and asymmetrical division. The migration of neurons forms a pre-cortical plate, and in the middle of gestation, immature neocortical neurons have extended axons and begin to form dendrites, beginning a long period of axonal growth, dendrite branching and synapse formation, continuing until early childhood. Synapse formation and axonal myelination are key cellular features of functional maturation of the central nervous system.
Research shows that in order to dynamically adapt to the needs of fetuses in different stages of pregnancy, the metabolic homeostasis of the mother has a specific fluctuation law in different stages of pregnancy. Abnormal metabolic fluctuations in Maternal Plasma (MP) often indicate that the fetal developing environment within the uterus is potentially at risk, possibly leading to adverse consequences such as Preeclampsia (PE) and Fetal Growth Restriction (FGR). At present, research is conducted to consider that the prediction markers related to adult brain injury are used more than one protein marker such as S100B, MBP, GFAP, NSE and the like, so that the prediction markers can be popularized to the prediction of intrauterine brain injury, but the prediction efficiency is low, the overall situation under the disease state can not be comprehensively reflected by the diagnosis efficiency of a single index, and the clinical application is difficult. The risk of MCDA twin distant neurocognitive behavioral dysplasia is significantly higher than other twin gestations, and is a cerebral palsy after birth when severe. At present, diagnosis of MCDA neonatal brain injury only depends on ultrasonic or nuclear magnetic examination results in middle or late pregnancy, but due to special intrauterine states, prenatal imaging examination has large limitations, which prevents early intervention of MCDA neonatal brain injury clinically. There is no related study report on related metabolic markers for predicting the occurrence of neonatal brain injury in MCDA twins. Therefore, a high-efficiency biomarker capable of assisting in early-stage clinical screening of fetal brain injury of one of the twin-fetuses is needed to achieve the purposes of early-stage screening and early-stage prevention and treatment, and is a good-care pilot.
Disclosure of Invention
The invention aims to provide a system and a method for predicting fetal brain injury by four factors in early and middle gestation, which at least aim to solve the technical problems of screening how to simultaneously realize the risk of occurrence of fetal brain injury in twin gestation, and screening abnormal fetuses which cannot be judged by potential imaging so as to perform early intervention and improve clinical prognosis.
In order to achieve the above object, the present invention provides a system for predicting fetal brain injury by early and middle gestation four factors, comprising a detection sample acquisition unit, a detection sample injection unit, a concentration acquisition unit of target metabolites, a data analysis and risk prediction model construction unit and a judgment standard setting unit;
the detection sample acquisition unit is used for acquiring peripheral blood plasma of the pregnant woman in early pregnancy and middle pregnancy as a detection sample;
the detection sample injection unit is used for injecting the detection sample into a QTRAP 6500plus LC-MS/MS system;
the concentration acquisition unit of the target metabolite is used for acquiring quantitative information of the concentration of the target metabolite in the peripheral blood plasma;
the data analysis and risk prediction model construction unit is used for carrying out inter-group significant difference metabolite identification and constructing a prediction model of the occurrence risk of the brain injury of the twin gestation fetus through logistic regression analysis;
the identification of the metabolites with significant differences among the groups comprises the steps of performing inter-group comparison of normally distributed metabolite concentration data by adopting an independent sample t-test, and performing inter-group non-parameter comparison of non-normally distributed metabolite concentration data by adopting a Mannheim U test; the same metabolite in different pregnancy periods is respectively unfolded for comparison;
the prediction model for the occurrence risk of the brain injury of the twin gestation fetus is constructed by logistic regression analysis and comprises a basePerforming a two-term logistic regression analysis on the log2 transformed values of the absolute concentrations of the selected inter-group significantly different metabolites to calculate the probability of brain injury occurrence of the twin gestation fetuses from the four identified metabolites with significant inter-group differences; wherein, taking the absolute concentration of four metabolites as an independent variable X, the logistic regression probability function is: p (y= 1|X) =；
The probability function represents the probability of fetal brain injury occurring (y=1) given X.
Wherein, x= [ X1, X2, X3, X4] is an independent variable matrix; z=wx+w0; w= [ W1, W2, W3, W4] is a weight coefficient matrix corresponding to X; w0 is a constant term.
The judging standard setting unit is used for setting an optimal judging threshold value, and judging that fetal brain damage occurs if the probability is larger than the optimal judging threshold value.
Preferably, the early pregnancy period of the pregnant woman is 8 weeks to 10 weeks of pregnancy, and the middle pregnancy period is 20 weeks to 22 weeks of pregnancy.
Preferably, the targeted metabolites include L-serine, L-histidine, L-arginine and creatinine.
Preferably, targeted metabolites with P-values less than 0.05 are defined as significantly different metabolites between groups.
Preferably, the concentration obtaining unit of the target metabolite adopts QTRAP 6500plus LC-MS/MS system software to combine skyline software, and quantitatively detects the concentration of the target metabolite in blood plasma.
Preferably, the data analysis and risk prediction model construction unit uses "shape-Wilktest" in SPSS for a normalization test, and performs an inter-group difference analysis comparison by an independent sample t test and a mann-whitney U test.
Preferably, the data analysis and risk prediction model construction unit uses the "glm" function in the R software package "stats" to perform logistic regression analysis.
Preferably, the data analysis and risk prediction model construction unit uses a "stepAIC" function in the R software package MASS to optimize a prediction model of brain damage occurrence of a twin gestation fetus.
Preferably, the four metabolites include three early gestational metabolites and one mid gestational metabolite.
Preferably, the three early gestation metabolites include L-serine, L-histidine and L-arginine.
Preferably, said one metaphase metabolite is creatinine.
Preferably, the weight coefficient matrix is W= [ -1.484, -5.107, 9.644, -2.471]; the constant term w0 is 1.441.
Preferably, the optimal decision threshold is 0.278.
The invention also provides a method for predicting fetal brain injury by four factors in early and middle gestation, which is essentially a method for screening the risk of onset of fetal brain injury of one neonate with single chorion and double amniotic sac (MCDA), and comprises the following steps:
s1, obtaining peripheral blood plasma of a pregnant woman in early pregnancy and middle pregnancy as a detection sample;
s2, injecting the detection sample into a QTRAP 6500plus LC-MS/MS system;
s3, acquiring quantitative information of the concentration of targeted metabolites (L-serine, L-histidine, L-arginine and creatinine) in the peripheral blood plasma;
s4, data analysis and risk prediction model construction:
s4.1, identification of significant differential metabolites between groups: performing inter-group comparison of normally distributed metabolite concentration data by adopting an independent sample t-test, and performing inter-group non-parameter comparison of non-normally distributed metabolite concentration data by adopting a Mannheim U test; the same metabolite in different pregnancy periods is respectively unfolded for comparison; metabolites with P values less than 0.05 were defined as significantly different metabolites between groups;
s4.2, constructing a prediction model of brain injury occurrence of the twin gestation fetus by logistic regression analysis:
the two comparison groups in the construction of the fetal brain injury occurrence prediction model of the twin pregnancy are a brain injury group and a non-brain injury twin; based on the four metabolites identified as having significant inter-group differences, performing a two-term logistic regression analysis on log2 transformed values of absolute concentrations of the selected inter-group significantly different metabolites to calculate the probability of brain injury incidence of the twin gestation fetuses;
taking the absolute concentration of four metabolites as an independent variable X, the logistic regression probability function is: p (y= 1|X) =The method comprises the steps of carrying out a first treatment on the surface of the The logistic regression probability function represents the probability of the fetus developing brain injury, y=1, given X; wherein x= [ X1, X2, X3, X4]]Is an independent variable matrix; z=wx+w0; w= [ W1, W2, W3, W4]]The weight coefficient matrix corresponding to X is obtained; w0 is a constant term.
S4.3, judging standard: an optimal judgment threshold is set, and if the probability is greater than the optimal judgment threshold, it is judged that fetal brain damage occurs.
Preferably, a QTRAP 6500plus LC-MS/MS system is used in step S2 above.
Preferably, the concentration of the targeted metabolites (L-serine, L-histidine, L-arginine and creatinine) in the plasma is quantitatively determined in step S3 above using QTRAP 6500plus LC-MS/MS system software in combination with skyline software.
Preferably, the above-described step S4.1 uses the "shape-Wilktest" in SPSS for the normalization test, and the independent sample t-test and the Mann-Whitney U test (Mann-Whitney U test) for the inter-group differential analysis comparison.
Preferably, the software for logistic regression analysis in step S4.2 above is a "glm" function in the R package "stats".
Preferably, the software for the optimal selection of the prediction model for brain damage occurrence in the twin gestation fetus in step S4.2 above is a "stepAIC" function in the R software package MASS.
Preferably, the four metabolite combinations described in step S4.2 above are: three early gestation metabolites: l-serine, L-histidine and L-arginine, and a metaphase metabolite: creatinine.
Preferably, the weight coefficient matrix in the step S4.2 is W= [ -1.484, -5.107, 9.644, -2.471]; W0 is 1.441.
Preferably, the optimal decision threshold described in step S4.3 above is 0.278.
Compared with the prior art, the invention has the beneficial effects that:
the system and the method for predicting the brain damage by four factors in early and middle gestation can be used for screening the risk of the brain damage of one neonate of single chorion double amniotic sac double embryo (MCDA), and LC-MS/MS targeting metabonomics is used for assisting in clinic to predict the risk of the fetal brain damage of at least one fetus in early and middle gestation period in advance in perinatal period so as to screen abnormal fetuses which cannot be judged by potential imaging for early intervention and improve clinical prognosis.
Compared with the prior art, the invention has the following remarkable improvements:
1. the method disclosed in the present invention can predict MCDA neonatal brain injury in advance, providing a time window for early intervention.
2. The method provided by the invention is suitable for predicting MCDA twins, and the existing method for assessing brain injury in clinic at present usually uses craniocerebral ultrasound or partially uses fetal craniocerebral magnetism to assess fetal abnormality, but no biological prediction index which can be used in clinic at present is used for predicting the brain injury problem of MCDA neonates, especially brain injury in sFGR fetuses, so that early intervention of the fetuses in clinic is difficult to implement.
3. The invention brings the real MCDA neonatal long-term ending into clinic for the first time, the prediction model is closer to the real situation, the prediction model has higher success rate in the prediction of the MCDA neonatal brain injury, and the invention also has good effect in practical clinical application.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
FIG. 1 is a flow chart of a method for predicting fetal brain injury by using early and middle gestation tetra factors according to the invention.
Fig. 2 is a graph of ROC corresponding to example 1.
Fig. 3 is a graph of ROC corresponding to example 2.
Detailed Description
The present invention is described in more detail below to facilitate an understanding of the present invention.
Example 1 detection method and prediction model construction
The technical route of the detection method is shown in figure 1. The specific description is as follows:
1. sample collection
A peripheral blood sample of the pregnant woman was collected and centrifuged at 1650 rcf (g) at 4℃for 10 minutes, and plasma was collected and stored at-80 ℃. Peripheral blood of pregnant women in early pregnancy is collected at 8-10 weeks, and peripheral blood of pregnant women in middle pregnancy is collected at 20-22 weeks.
2. Metabolite extraction
Plasma (20 μl) was aliquoted into 1.5 ml centrifuge tubes and mixed with 80 μl internal standard in methanol. Proteins in the sample were precipitated by vortexing for 1min at 4-8 ℃, the supernatant recovered by centrifugation at 20,000g for 10min at 4 ℃ and 1 μl was injected into QTRAP 6500plus LC-MS/MS system.
Extracting amino acid:
(1) The supernatant (3 μl) was injected into an amino acid UPLC column (Intra analytical column, 100 x3 mm i.d.,3 μm; imtakt, kyoto, japan) using Shimadzu LC-20AD at a flow rate of 0.5mL/min for analysis, and the SIL-20AXR autosampler was interfaced with an API 6500Q-TRAP mass spectrometer (SCIEX, framingham, mass.).
(2) A discontinuous gradient was created by mixing solvent a (100 mM ammonium formate in water) with solvent B (0.1 wt% formic acid in acetonitrile) to separate the analytes.
(3) Gradient elution was optimized for single amino acid separation at a flow rate of 0.5mL/min, column temperature maintained at 40 ℃:
1) 0-0.5 min, 80% solution B;
2) 0.5-4.5 min, 80% -70% solution B;
3) 4.5 to 5.0min,70 to 40 percent of solution B;
4) 5.0-10.0min 40% -0 solution B;
5) 10.0 to 13.0min 0 of solution B;
6) 13.0-13.2 minutes 0-80% solution B, total run time 13.2 minutes.
7) Electrospray ionization is used to monitor analytes in negative ion mode, while multi-reaction monitoring (MRM) is performed on precursor and feature product ion transitions of amino acids.
Extraction of creatinine pathway procedure:
(1) The analysis was performed by injection into a silica gel column (2.0x150 mm,Luna 5u Silica 100A;Phenomenex,Torrance,CA), and a SIL-20AXR autosampler was interfaced with an API 6500Q-TRAP mass spectrometer (SCIEX, framingham, mass.).
(2) A discontinuous gradient was created by mixing solvent a (0.1% aqueous propionic acid) and solvent B (0.1% methanol acetate) to separate the analytes.
(3) Gradient elution was optimized for separation with a flow rate of 0.5mL/min, column temperature maintained at 35 deg.c:
1) 0.5min,2% solution B;
2) 5.0min,2% -95% solution B;
3) 5.0min,95% solution B;
4) 6.0min,95% -2% solution B, total run time 6.5mins;
5) Analytes were monitored in negative ion mode using electrospray ionization while multi-reaction monitoring (MRM) was performed on product ion transitions.
3. Obtaining absolute concentration data of the targeted metabolite: quantitative analysis of targeted metabolites (L-serine, L-histidine, L-arginine and creatinine) in plasma was achieved by collecting characteristic parent and fragment ions of the metabolites, combining retention time information, calculating the peak area of the color spectrum using 6500 software in combination with skyline software.
4. Construction of a fetal brain injury prediction model for twin pregnancy
(1) Identification of significant differences between groups: the 25 target metabolite concentrations of plasma in early and middle gestation period of 22 normal twin pregnant women and pregnant women with brain injury of at least one fetus of 9 twin obtained in clinic are taken as data sets. To find combinations of metabolites that could indicate the occurrence of fetal brain injury in both births, first two groups of significantly different metabolites were identified. The method comprises the following steps: for each metabolite in any pregnancy, performing normal distribution metabolite concentration data by adopting an independent sample t test, and performing non-parameter comparison between fetal brain injury groups of one of the two tires of the non-normal distribution metabolite concentration data by adopting Mann-Whitney U; metabolites with p-values less than 0.05 were defined as significantly different metabolites between groups. A total of 4 maternal plasma metabolites were obtained at early (n=3) or mid (n=1) gestation that exhibited significant inter-group differences between normal twin and the group with brain damage. Their absolute concentration levels are a good indicator of the occurrence or absence of brain damage in a twin fetus (as shown in Table 1).
Table 1: identified highly indicative inter-group significantly different metabolites
(3) Further, using the above 22 normal twin pregnant women and 9 pregnant women with fetal brain damage, early and mid gestation plasma as samples, using the absolute concentrations of 4 metabolites shown in table 1 as variables, a logistic regression model was constructed. The method comprises the following steps: setting x= [ X1, X2, X3, X4]]As an independent variable matrix, w= [ W1, W2, W3, W4]]As a weight coefficient matrix, a linear function z=wx+w0 is obtained. Further, a probability function P (y= 1|X) =is obtained. Wherein the weight coefficient matrix W is obtained by fitting the data of early and middle period of gestation plasma samples of the first 22 normal double pregnant women and 9 pregnant women with fetal brain injury, and the matrix is W= [ -1.484, -5.107, 9.644, -2.471]W0 is 1.441.
(4) The data used were incorporated into the fetal brain injury prediction model of the twin pregnancy described in the present invention, predicted values of the data themselves were obtained and ROC subjects were constructed using the "ROC" and "ggroc" functions of the R-package "pROC, the area under the ROC curve was calculated and the ROC curve was plotted, the AUC of the area under the ROC curve was 0.924, 95% CI: 0.833-1 (DeLong), see specifically fig. 2. The circles in FIG. 2 represent the optimal knowledge threshold (cut-0 ff value), and the suffix brackets indicate the true positive rate ((True Positive Rate, TPR)) and false positive rate (False Positive Rate, FPR) for that value.
Where 95% CI (Confidence Interval) represents a 95% confidence interval, which is the uncertainty range of the AUC estimation, means that there is 95% confidence, based on the sample data, that the true value of the estimated AUC falls within this interval. In general, a narrower confidence interval indicates a higher accuracy in the AUC estimation. AUC represents the area enclosed by the axis of the ROC curve.
The DeLong method is a common non-parametric method for estimating the 95% confidence interval of AUC. This approach does not rely on assumptions about the data distribution, which generates multiple AUC values by boottrap sampling, and then calculates the confidence interval for AUC from the distribution of these values.
The optimal cut-off value is determined to be 0.278 according to the Youden index (the sum of sensitivity and specificity minus 1), and prediction classification is carried out according to the optimal cut-off value of 0.278, at this time, the accuracy of the correct classification of dependent variables by the twin pregnancy fetal brain injury prediction model is 87.1%, the sensitivity is 88.89% and the specificity is 86.36%.
(5) And calculating the probability of fetal brain injury of one of the twin fetuses by using the probability model obtained in the last step aiming at the newly obtained plasma sample.
Example 2 predictive screening of the occurrence of brain injury in a gestation fetus with the method
The experiment was carried out with one of 14 twin fetuses for fetal brain injury and 5 normal MCDA twin pregnant women, peripheral blood was collected at 8-10 weeks for pregnant women at early gestation period, peripheral blood was collected at 20-22 weeks for pregnant women at mid gestation period, and plasma was separated by centrifugation at 1650 rcf (g) for 10 minutes at 4 ℃.
1. Metabolite extraction
Plasma (20 μl) was aliquoted into 1.5 ml centrifuge tubes and mixed with 80 μl internal standard in methanol. Proteins in the sample were precipitated by vortexing for 1min at 4-8 ℃, the supernatant recovered by centrifugation at 20,000g for 10min at 4 ℃ and 1 μl was injected into QTRAP 6500plus LC-MS/MS system.
1.1 extraction of amino acids (L-serine, L-histidine, L-arginine):
(1) The supernatant (3 μl) was injected into an amino acid UPLC column (Intra analytical column, 100 x3 mm i.d.,3 μm; imtakt, kyoto, japan) using Shimadzu LC-20AD at a flow rate of 0.5mL/min for analysis, and the SIL-20AXR autosampler was interfaced with an API 6500Q-TRAP mass spectrometer (SCIEX, framingham, mass.).
(2) A discontinuous gradient was created by mixing solvent a (100 mM ammonium formate in water) with solvent B (0.1% formic acid in acetonitrile) to separate the analytes.
(3) Gradient elution was optimized for single amino acid separation at a flow rate of 0.5mL/min, column temperature maintained at 40 ℃:
1) 0-0.5 min, 80% solution B;
2) 0.5-4.5 min, 80% -70% solution B;
3) 4.5 to 5.0min,70 to 40 percent of solution B;
4) 5.0-10.0min 40% -0 solution B;
5) 10.0 to 13.0min 0 of solution B;
6) 13.0-13.2 minutes 0-80% solution B, total run time 13.2 minutes.
7) Electrospray ionization is used to monitor analytes in negative ion mode, while multi-reaction monitoring (MRM) is performed on precursor and feature product ion transitions of amino acids.
1.2 procedure for extracting creatinine:
(1) The analysis was performed by injection into a silica gel column (2.0x150 mm,Luna 5u Silica 100A;Phenomenex,Torrance,CA), and a SIL-20AXR autosampler was interfaced with an API 6500Q-TRAP mass spectrometer (SCIEX, framingham, mass.).
(2) A discontinuous gradient was created by mixing solvent a (0.1% aqueous propionic acid) and solvent B (0.1% methanol acetate) to separate the analytes.
(3) Gradient elution was optimized for separation with a flow rate of 0.5mL/min, column temperature maintained at 35 deg.c:
1) 0.5min,2% solution B;
2) 5.0min,2% -95% of solution B;
3) 5.0min,95% solution B;
4) 6.0min,95% -2% solution B, total run time 6.5mins;
5) Analytes were monitored in negative ion mode using electrospray ionization while multi-reaction monitoring (MRM) was performed on product ion transitions.
2. Obtaining absolute concentration data of the targeted metabolite: the quantitative analysis of the targeted metabolites (L-serine, L-histidine, L-arginine and creatinine) in the plasma is realized by collecting characteristic parent ions and fragment ions of the metabolites and combining retention time information and calculating the peak area of the color spectrum by using QTRAP 6500plus LC-MS/MS system software and combining skyline software. As shown in table 2 below.
Table 2: four metabolite concentrations (in. Mu.M, log2 (absolute +1) for 19 pregnant women)
3. Probability calculation and prediction of fetal brain damage occurrence using early and mid-gestation plasma metabolite predictive models
These values were further substituted into the probability prediction function for occurrence of fetal brain injury in the twin pregnancy constructed in 4 (3) of the foregoing example 1 to obtain probabilities of occurrence of fetal brain injury in 19 twin pregnancy pregnant women, and cases of 19 twin pregnancy pregnant women were predicted with the best judgment threshold value 0.278 determined in 4 (3) of the foregoing example 1 as the judgment threshold value, with specific results shown in the following table 3:
table 3: probability of occurrence and prediction result of brain injury of 19 twin gestation fetuses
4. The results of the pregnancy outcome versus the predicted outcome are shown in table 4 below:
and counting whether at least one fetus of the 19 pregnant women with twin pregnancy has brain injury, constructing ROC objects of real pregnancy outcome and prediction probability by using the functions of ROC and ggroc of pROC R package, calculating the area under the ROC curve and drawing the ROC curve, wherein the area under the ROC curve AUC is 0.929, 95% CI: 0.8014-1 (DeLong), and particularly shown in figure 3. Comparing the result with the predicted result, it can be found that the pregnancy ending of 14 pregnant women in total accords with the prediction, the pregnancy ending of 5 pregnant women does not accord with the prediction, and the overall accuracy of the prediction is 73.68%.
Table 4: comparison of true and predicted pregnancy outcomes for 19 miscarriage fetal brain lesions
According to the results, the application mainly focuses on 4 metabolites with highest correlation degree with fetal brain injury occurrence in twin pregnancy, and successful screening of the bad prognosis of MCDA twin is carried out through combined prediction of the 4 metabolites, so that a single chorion twin amniotic sac twin (MCDA) twin-twin bad prognosis prediction model constructed by the application is proved to be truly effective in clinical application.
Based on the above embodiments, the present invention provides a method for predicting brain damage by early and middle gestation four factors, which is essentially a method capable of realizing screening of risk of developing brain damage of one neonate of single chorion double amniotic sac double embryo (MCDA), comprising the following steps:
s1, obtaining peripheral blood plasma of pregnant women in early pregnancy (8-10 weeks of pregnancy) and middle pregnancy (20-22 weeks of pregnancy) as detection samples;
s2, injecting an outer Zhou Xiejiang sample into a QTRAP 6500plus LC-MS/MS system;
s3, acquiring quantitative information of the concentration of targeted metabolites (L-serine, L-histidine, L-arginine and creatinine) in the peripheral blood plasma;
s4, data analysis and risk prediction model construction:
s4.1, identification of significant differential metabolites between groups: performing inter-group comparison of normally distributed metabolite concentration data by adopting an independent sample t-test, and performing inter-group non-parameter comparison of non-normally distributed metabolite concentration data by adopting a Mann-Whitney U test; the same metabolite in different pregnancy periods is respectively unfolded for comparison; metabolites with P values less than 0.05 were defined as significantly different metabolites between groups.
S4.2, constructing a prediction model of brain injury occurrence of the twin gestation fetus by using logistic regression analysis:
the two comparison groups in the construction of the fetal brain injury occurrence prediction model for twin pregnancy are a brain injury group and a non-brain injury twin group respectively. Based on the identification of four metabolites with significant inter-group differences, a two-term logistic regression analysis was performed on log2 transformed values of absolute concentrations of the selected inter-group significantly different metabolites to calculate the probability of brain damage to the twin gestation fetuses.
Taking the absolute concentration of four metabolites as an independent variable X, a logistic regression probability function is: p (y= 1|X) =. The logistic regression probability function represents the probability that a selective growth restriction (y=1) occurs given X. Wherein x= [ X1, X2 ]]Is an independent variable matrix; z=wx+w0; w= [ W1, W2]The weight coefficient matrix corresponding to X is obtained; w0 is a constant term (also known as intercept or random error term).
S4.3, judging standard: an optimal judgment threshold is set, and if the probability is greater than the optimal judgment threshold, it is judged that fetal brain damage occurs.
In one embodiment, a QTRAP 6500plus LC-MS/MS system is used in step S2 above.
In one embodiment, the concentration of the targeted metabolites (L-serine, L-histidine, L-arginine and creatinine) in the plasma is quantitatively determined using QTRAP 6500plus LC-MS/MS system software in combination with skyline software in step S3 above.
In one embodiment, the above step S4.1 uses the "shape-Wilktest" in SPSS for a normalization test, and the independent sample t-test and the Mann-Whitney U test (Mann-Whitney U test) for an inter-group differential analysis comparison.
In one embodiment, the software for logistic regression analysis in step S4.2 above is the "glm" function in the R package "stats".
In one embodiment, the software used in step S4.2 above for the optimal selection of the prediction model for brain damage occurrence in a twin gestation fetus is a "stepAIC" function in the R software package MASS.
In one embodiment, the four metabolite combinations described in step S4.2 above are: three early gestation metabolites: L-Serine, L-Histidine and L-Arginine, and a metaphase metabolite: creatinine (Creatinine).
In one embodiment, the weight coefficient matrix in the step S4.2 is W= [ -1.484, -5.107, 9.644, -2.471]; W0 is 1.441.
In one embodiment, the optimal decision threshold described in step S4.3 above is 0.278.
The invention also provides a system for predicting brain damage by four factors in early and middle gestation, which is essentially a system capable of realizing screening of the risk of onset of brain damage of one neonate of single chorion double amniotic sac (MCDA), and comprises a detection sample acquisition unit, a detection sample injection unit, a concentration acquisition unit of a target metabolite, a data analysis and risk prediction model construction unit and a judgment standard setting unit;
the detection sample acquisition unit is used for acquiring peripheral blood plasma of pregnant women in early pregnancy (8-10 weeks of pregnancy) and middle pregnancy (20-22 weeks of pregnancy) as detection samples;
the detection sample injection unit is used for injecting the detection sample into a QTRAP 6500plus LC-MS/MS system;
the concentration acquisition unit of the target metabolite is used for acquiring quantitative information of the concentration of the target metabolite (L-serine, L-histidine, L-arginine and creatinine) in the peripheral blood plasma;
the data analysis and risk prediction model construction unit is used for carrying out inter-group significant difference metabolite identification and constructing a prediction model of brain injury occurrence of the twin gestation fetus through logistic regression analysis;
the identification of the metabolites with significant differences among the groups comprises the steps of performing the inter-group comparison of normally distributed metabolite concentration data by adopting an independent sample t test, and performing the inter-group non-parameter comparison of non-normally distributed metabolite concentration data by adopting a Mann-Whitney U test; the same metabolite in different pregnancy periods is respectively unfolded for comparison; metabolites with P values less than 0.05 were defined as significantly different metabolites between groups;
the construction of the prediction model of the brain injury occurrence of the twin-gestation fetus by the logistic regression analysis comprises the steps of carrying out two-term logistic regression analysis on log2 conversion values of absolute concentration of the metabolites with significant differences among the selected groups based on the four identified metabolites with significant differences among the groups so as to calculate the probability of the brain injury occurrence of the twin-gestation fetus; wherein, taking the absolute concentration of four metabolites as an independent variable X, the logistic regression probability function is: p (y= 1|X) =；
X= [ X1, X2, X3, X4] is an argument matrix; z=wx+w0; w= [ W1, W2, W3, W4] is a weight coefficient matrix corresponding to X; w0 is a constant term;
the judging standard setting unit is used for setting an optimal judging threshold value, and judging that fetal brain damage occurs if the probability is larger than the optimal judging threshold value.
Preferably, the two comparison groups in the construction of the fetal brain injury occurrence prediction model for twin pregnancy are a brain injury group and a non-brain injury twin group respectively.
Preferably, the logistic regression probability function represents the probability that, given X, one of the fetuses, i.e. y=1, is suffering from brain injury.
The method for predicting fetal brain injury by using the early and middle gestation four factors can complete the disease occurrence prediction of the fetal brain injury in the perinatal period of one neonate of the single chorion and the double amniotic sac, and greatly improve the early detection rate of the MCDA double-fetal bad nerve prognosis, the prediction model is close to the clinical actual condition, and the method has higher success rate in the MCDA fetal perinatal brain injury risk prediction, has good effect in the actual clinical application, and has extremely high popularization value.
The foregoing describes preferred embodiments of the present invention, but is not intended to limit the invention thereto. Modifications and variations to the embodiments disclosed herein may be made by those skilled in the art without departing from the scope and spirit of the invention.
Claims (10)
1. The system for predicting the fetal brain injury by the early and middle gestation four factors is characterized by comprising a detection sample acquisition unit, a detection sample injection unit, a concentration acquisition unit of a target metabolite, a data analysis and risk prediction model construction unit and a judgment standard setting unit;
the detection sample acquisition unit is used for acquiring peripheral blood plasma of the pregnant woman in early pregnancy and middle pregnancy as a detection sample;
the detection sample injection unit is used for injecting the detection sample into a QTRAP 6500plus LC-MS/MS system;
the concentration acquisition unit of the target metabolite is used for acquiring quantitative information of the concentration of the target metabolite in the peripheral blood plasma;
the data analysis and risk prediction model construction unit is used for carrying out inter-group significant difference metabolite identification and constructing a prediction model of the occurrence risk of the brain injury of the twin gestation fetus through logistic regression analysis;
the identification of the metabolites with significant differences among the groups comprises the steps of performing inter-group comparison of normally distributed metabolite concentration data by adopting an independent sample t-test, and performing inter-group non-parameter comparison of non-normally distributed metabolite concentration data by adopting a Mannheim U test; the same metabolite in different pregnancy periods is respectively unfolded for comparison;
the construction of the prediction model of the brain injury occurrence risk of the double pregnant fetus by the logistic regression analysis comprises the steps of carrying out two-term logistic regression analysis on log2 conversion values of absolute concentrations of the metabolites with significant differences among the selected groups based on the four identified metabolites with significant differences among the groups so as to calculate the probability of the brain injury occurrence risk of the double pregnant fetus; wherein, taking the absolute concentration of four metabolites as an independent variable X, the logistic regression probability function is:
;
x= [ X1, X2, X3, X4] is an argument matrix; z=wx+w0; w= [ W1, W2, W3, W4] is a weight coefficient matrix corresponding to X; w0 is a constant term; the four metabolites include L-serine, L-histidine, L-arginine and creatinine;
the judging standard setting unit is used for setting an optimal judging threshold value, and judging that fetal brain damage occurs if the probability is larger than the optimal judging threshold value.
2. The system for predicting fetal brain injury by four factors of early and middle gestation of claim 1, wherein the early pregnancy of the pregnant woman is 8 weeks to 10 weeks of gestation, and the middle pregnancy is 20 weeks to 22 weeks of gestation.
3. The system for predicting fetal brain injury of claim 1, wherein the targeted metabolites comprise L-serine, L-histidine, L-arginine and creatinine.
4. The system for predicting fetal brain injury by midterm tetra-factor of gestation of claim 1, wherein targeted metabolites having a P-value of less than 0.05 are defined as significantly different metabolites between groups.
5. The system for predicting fetal brain injury by using early and middle gestation four factors according to claim 1, wherein the concentration obtaining unit of the target metabolite adopts QTRAP 6500plus LC-MS/MS system software and skyline software to quantitatively detect the concentration of the target metabolite in blood plasma.
6. The system for predicting fetal brain injury according to claim 1, wherein the data analysis and risk prediction model construction unit performs a normalization test using "Shapiro-Wilktest" in SPSS and performs an inter-group difference analysis comparison by an independent sample t-test and a mann-whitney U-test.
7. The system for predicting fetal brain injury by four factors of early and medium gestation according to claim 1, wherein the data analysis and risk prediction model construction unit uses the "glm" function in the R software package "stats" to perform logistic regression analysis.
8. The system for predicting fetal brain injury from early and mid gestation four factors according to claim 1, wherein said data analysis and risk prediction model construction unit optimizes a twin gestation fetal brain injury occurrence prediction model using a "stepai" function in R software package MASS.
9. The system for predicting fetal brain injury by four factors in early and middle gestation according to claim 1, wherein the weight coefficient matrix is w= [ -1.484, -5.107, 9.644, -2.471]; the constant term w0 is 1.441.
10. The system for predicting fetal brain injury by four factors of early and middle gestation of claim 1, wherein the optimal decision threshold is 0.278.
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