Marker for predicting premature delivery risk and application

Abstract

The invention discloses a marker for predicting the risk of premature delivery, and the marker is an amniotic fluid metabolite which is selected from one or more of Corydaline, Diethanolamine and Loureirinb. The prediction method comprises the following steps: analyzing the expression quantity of metabolites in a maternal amniotic fluid sample, and predicting whether a subject has the risk of premature delivery or not according to the expression quantity. The kit is used for detecting the expression quantity of metabolite with high marking effect in an amniotic fluid sample of a pregnant woman, is high in sensitivity and specificity, is used as an auxiliary means for predicting the premature delivery risk of the pregnant woman, is used as a diagnosis means for the pregnant woman with the premature delivery risk or premature delivery, improves the pregnancy outcome of the pregnant woman, improves the pregnancy quality of the pregnant woman, and reduces the labor intensity of the pregnant woman. The occurrence of neonatal adverse events is reduced; according to the invention, the variety of biomarkers for predicting the premature delivery risk is expanded.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine and relates to a biomarker for predicting the risk of preterm birth and its applications. Background Art

[0002] Preterm birth (PTB), as a common pregnancy complication, refers to the delivery that occurs between 28 and 37 weeks of gestation. The proportion of preterm birth in global pregnancy outcomes is approximately 12%. There are about 15 million preterm births globally every year, and the preterm birth rate reaches 10.6%. Due to preterm birth and its associated complications, approximately 1.1 million newborns lose their lives, accounting for more than half of the total number of neonatal deaths. Moreover, preterm birth is not only the leading cause of neonatal death globally but also the second major cause of death among children under 5 years old. In addition, the incidence of related diseases in preterm infants who survive after treatment is much higher than that in full-term infants, including neurological diseases (such as cognitive impairment), movement disorders, cardiovascular and cerebrovascular diseases, metabolic diseases, endocrine diseases, kidney diseases, etc. This seriously affects the quality of life of preterm infants, brings huge psychological pressure and economic burden to family members, has a certain impact on the quality of the national birth population, and causes medium- and long-term economic losses.

[0003] The "Global Burden of Disease Study" in 2010 proposed that preterm birth is the independent disease factor with the heaviest global health burden based on two major factors: high lethality and long-term damage to an individual's lifelong health, emphasizing the profound impact of preterm birth on the global health status. Preterm birth is generally recognized as a syndrome jointly caused by various factors such as sociodemographic factors, obstetric and gynecological conditions, genetic background, immune and inflammatory responses, metabolic problems, psychological state, and lifestyle. Some studies have revealed the key role of specific biological pathways in preterm birth. Some specific effector molecules such as interferon-γ (IFN-γ) and interleukin-6 (IL-6) contribute to the occurrence of preterm birth by stimulating the destruction of the structure of the maternal-fetal interface and triggering early contractions of the uterine muscles. In addition, the influencing factors of preterm birth cover a variety of potential molecular biological changes. If these changes can be detected and evaluated early, it will be helpful for the clinical diagnosis, treatment, and intervention of preterm birth and further improve the final outcome of the mother and fetus. Given the significant impact of preterm birth on personal health and economic society, especially with the liberalization of the national fertility policy and the increasing rate of preterm birth brought about by the increase in the number of elderly pregnant women, using metabolomics to reveal preterm birth-related biomarkers and establish a prediction model has important clinical significance for the early diagnosis, treatment, and prevention of preterm birth and the improvement of neonatal survival rate.

[0004] With the rapid development of high-throughput sequencing technology, fields such as genomics, transcriptomics, proteomics, and metabolomics have become the forefront of precision medicine development. These technologies not only show great potential in developing biomarkers related to preterm birth but also provide strong support for in-depth analysis of the regulatory mechanisms of preterm birth. For this multi-factorial syndrome of preterm birth, it is particularly important to use high-throughput technologies to analyze the molecular-level disorders, which can provide a scientific basis for revealing the complex biological mechanisms behind preterm birth and predicting preterm birth. Metabolomics conducts qualitative and quantitative analysis by detecting changes in endogenous metabolites of organisms, thereby reflecting the endogenous and exogenous changes and overall functional status under different states of the body. It is one of the components of systems biology and has obvious advantages compared with other omics methods. Its main method is to describe all the metabolic components of the samples under study and then relate their concentrations or peak intensities to the attributes or characteristics of the samples to find differential metabolites between the experimental group and the healthy group. According to different research modes, metabolomics is divided into non-targeted and targeted metabolomics. Non-targeted metabolomics is to comprehensively and systematically analyze the metabolome of the whole organism without bias on the basis of limited background knowledge, obtain a large amount of metabolite data, and discover differential metabolites from them. At present, non-targeted metabolomics analysis is widely used in aspects such as the metabolic dynamic changes, mechanism research, and biomarker discovery of diseases, and provides new ideas and directions for solving the bottleneck problems in the research of some disease mechanisms. Obviously, the non-targeted metabolomics method can obtain the most comprehensive information on metabolites related to preterm birth, and its ability to screen and determine the characteristics of unknown substances is more suitable for the search for preterm birth biomarkers.

[0005] In addition, the research platforms of metabolomics include hydrogen / carbon nuclear magnetic resonance technology, gas chromatography-mass spectrometry technology, liquid chromatography-mass spectrometry technology, capillary electrophoresis-mass spectrometry technology, and direct injection mass spectrometry technology, etc. Ultra-High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) is a modern comprehensive analysis technology that achieves two-dimensional separation based on the differences in the distribution of various analytical components in the chromatographic mobile phase and the two-phase differences in the mass-to-charge ratio of charged particles. It can maximize the combination of the high-performance physical separation ability of ultra-high performance liquid chromatography and the unique mass analysis ability of tandem mass spectrometry, and at the same time has high sensitivity and selectivity, which can provide a good research platform for the search for preterm birth biomarkers.

[0006] Regarding the metabolomics research on preterm birth, many scholars have analyzed amniotic fluid and found some metabolites that can be used as biomarkers for preterm birth, such as hormones estriol (E3), estetrol (E4), progesterone, prolactin, chorionic gonadotropin, cortisol, testosterone, and prostaglandin in amniotic fluid, and other amniotic fluid metabolites: 17-hydroxyprogesterone, methylmalonic acid, mucopolysaccharide, galactose, pyruvic acid, reverse triiodothyronine (RT3), etc. For example, the concentration of sTLR4 in amniotic fluid is closely related to intra-amniotic infection and preterm birth in pregnant women. Menon et al. analyzed the amniotic fluid of African American women with early spontaneous preterm birth and term delivery. The results showed that the main metabolic changes in amniotic fluid during spontaneous preterm birth were liver metabolism and CoA metabolism changes, among which the CoA synthesis inhibitor - ubiquinone changed the most. The concentration of some metabolites in the amniotic fluid of spontaneous preterm birth was more than 8 times higher than that of the control group. This indicates that amniotic fluid metabolomics analysis may become the biological basis for rapid detection of the risk of preterm birth (with or without infection / inflammation).

[0007] Although many clinical studies have tried to use combined indicators to predict preterm birth to improve the prediction effect. However, due to the clinical diversity and mechanism complexity of preterm birth, the sensitivity of prediction is generally low and the research conclusions are inconsistent. Establishing and validating a well-performing preterm birth prediction model will have greater clinical practical significance.

[0008] After retrieval, the amniotic fluid metabolites Corydaline, Diethanolamine, and Loureirinb have not been publicly reported as markers for predicting the risk of preterm birth. Summary of the Invention

[0009] Therefore, the present invention provides markers for predicting the risk of preterm birth and their applications.

[0010] The technical solution of the present invention is as follows:

[0011] The application of a marker for predicting the risk of preterm birth in the preparation of a reagent for predicting or evaluating the risk of preterm birth, wherein the marker is one or more of Corydaline, Diethanolamine, and Loureirinb.

[0012] Further, the reagent is a detection reagent for detecting the expression level of a biomarker related to the risk of preterm birth in a subject sample.

[0013] Further, the subject sample is the amniotic fluid of a pregnant subject.

[0014] The present invention also protects a kit, which comprises specific antibodies or mass spectrometry detection reagents for detecting Corydaline, Diethanolamine, and Loureirinb.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a new method for predicting the risk of preterm birth by using the expression levels of metabolic markers such as Corydaline, Diethanolamine, and Loureirinb in amniotic fluid. In particular, the combination of Corydaline, Diethanolamine, and Loureirinb is used for prediction, which improves the sensitivity and specificity of predicting the risk of preterm birth, has higher prediction accuracy, and can better meet the clinical needs; it expands the types of biomarkers used for predicting the risk of preterm birth; the risk of preterm birth can be predicted by analyzing the expression levels of markers in maternal amniotic fluid samples; this method is simple to operate, economical and practical, has good clinical application prospects, and is expected to become an auxiliary means widely used in the daily prenatal examinations of pregnant women. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a correlation analysis diagram of the expression levels of differential metabolites in amniotic fluid;

[0017] Figure 2 It is the variable selection result of the LASSO regression model, where Figure 2 A is the curve diagram of the change of LASSO regression coefficients, Figure 2 B is the cross-validation curve diagram of LASSO regression;

[0018] Figure 3 It is the ranking of the importance of differential metabolites in amniotic fluid, Figure 3 (Left) is the average importance ranking of the results of 100 random forest repetitions; Figure 3 (Right) The heat map shows the average importance of each differential metabolite in each random forest;

[0019] Figure 4 It is a Venn diagram of candidate biomarkers of amniotic fluid metabolites;

[0020] Figure 5 It is a scatter plot of the expression levels of candidate amniotic fluid metabolic markers;

[0021] Figure 6 It is a nomogram of candidate amniotic fluid metabolic markers;

[0022] Figure 7 It is the ROC analysis of candidate amniotic fluid metabolic markers. Among them, Figures A-F respectively represent Corydaline, Diethanolamine, Loureirinb, CorydalineVS combination, DiethanolamineVS combination, LoureirinbVS combination, and the red curve represents the combined biomarker model curve;

[0023] Figure 8 It is the calibration curve of the combined amniotic fluid metabolic marker prediction model. Detailed implementation manners

[0024] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the present invention is not limited to the following technical solutions.

[0025] The instruments and reagents used in the following examples are as follows: Q Exactive series mass spectrometers, Thermo Fisher (USA); Vanquish UHPLC ultra-high pressure liquid chromatographs, Thermo Fisher (USA); low-temperature high-speed centrifuges, HITACHI (Japan); chromatographic columns, Waters (USA); 80 °C ultra-low temperature refrigerators, Shandong Boke Scientific Instruments Co., Ltd.; acetonitrile, Merck (Germany); ammonium acetate, Sigma-Aldrich (USA); methanol, Sigma-Aldrich (USA); ammonia water, Fisher Scientific (USA); formic acid, Fisher Scientific (USA).

[0026] Sample source:

[0027] The inventors' project team carried out the study at the prenatal diagnosis center of the First People's Hospital of Yunnan Province. Epidemiological data of pregnant women before childbirth were obtained through questionnaires. The pregnancy outcomes of the surveyed pregnant women were obtained by consulting medical records and telephone follow-up. Amniotic fluid biological samples of preterm and term pregnant women were collected.

[0028] Inclusion criteria and exclusion criteria

[0029] Inclusion criteria: (1) Singleton pregnancy; (2) Complete data and well-preserved amniotic fluid samples; (3) Full informed consent to participate in the survey; (4) The preterm group meets the preterm diagnosis criteria, and the delivery gestational weeks of the pregnant women in the term group are 37 to 41 weeks.

[0030] Exclusion criteria: (1) Pregnancy complications such as preeclampsia, fetal growth restriction, macrosomia, fetal distress, etc.; (2) Taking psychiatric drugs such as antidepressants; (3) Stillbirth, birth defects, miscarriage, a large amount of missing information, etc.; (4) Pregnant women who are unwilling to participate in the survey or have not signed the informed consent form.

[0031] Exclude twins, pregnant women with gestational hypertension, gestational diabetes, and pregnant women with missing samples that do not meet the inclusion criteria. Amniotic fluid metabolomics detection was performed on 38 pregnant women.

[0032] Thirty-eight pregnant women meeting the criteria were included for amniotic fluid metabolite detection. Among them, there were 13 cases in the preterm birth group and 25 cases in the full-term birth group. The age range was 23 to 36 years old, with an average age of 29.8 ± 3.5 years old, and the average BMI was 22.6 ± 3.7. Twenty-seven were Han nationality, accounting for 71.1%, and 11 were ethnic minorities, accounting for 28.9%. The demographic characteristics of the research subjects are shown in Table 1. There were no statistically significant differences between the two groups of patients in terms of age, BMI (after pregnancy), gestational week at amniotic fluid collection, educational level, place of residence, ethnicity, occupation, fetal gender, and whether this was a planned pregnancy (P > 0.05). The average gestational week of delivery for pregnant women in the preterm birth group was 35.7 ± 0.4 weeks, and the average gestational week of delivery for pregnant women in the full-term birth group was 39.2 ± 1.1 weeks, with a statistically significant difference (P < 0.001).

[0033] Table 1 Demographic characteristics of the research subjects

[0034]

[0035] Note: a: T-test; b: Wilcoxon-Mann-Whitney test; the rest are χ 2 test.

[0036] Example 1 Screening of amniotic fluid metabolite markers, establishment of a preterm birth prediction model, and evaluation of the prediction model

[0037] By searching the HMDB and Metlin databases, 416 and 223 compounds were identified under the positive and negative ion modes of amniotic fluid respectively; the discrimination rate was about 5% - 10%. In order to obtain significantly differentially expressed metabolites between the preterm birth group and the full-term birth group, with FC > 1.2 or FC < 1 / 1.2 (0.83), VIP > 1 in OPLS-DA analysis and P < 0.05 in t-test as the screening criteria for differential metabolites, 34 differentially expressed metabolites with different expression levels were screened out from the amniotic fluid. Among them, the expression levels of 13 metabolites were significantly up-regulated, and the expression levels of 21 metabolites were significantly down-regulated; mainly including lipid metabolites, nucleotide metabolites, amino acid metabolites, etc.

[0038] Analysis of metabolite correlation

[0039] The relationship between the peaks of 34 amniotic fluid differential metabolites was evaluated by Pearson correlation analysis. The results showed that there was a relatively high correlation between some metabolites due to similar functions, and the correlation between most of the remaining metabolites was medium or below. The median correlation coefficient of amniotic fluid differential metabolites was 0.006( Figure 1 ), and the degree of correlation was represented by color in the matrix diagram. The darker the color, the stronger the correlation. Red indicates positive correlation, and blue indicates negative correlation.

[0040] LASSO regression and cross-validation method

[0041] Given the complex relationships among the differential metabolites identified in this study and their application value in preterm birth prediction, the "glmnet" package in R language software was used. Thirty-four amniotic fluid differential metabolites were used as input variables respectively to perform the LASSO regression algorithm, combined with a penalty function for variable selection and regularization to reduce the dimension of the model, so as to determine the most potential metabolic markers and optimize the model.

[0042] From Figure 2 A, it can be seen that when the logarithm of the penalty coefficient Log(λ) increases, the regression coefficients of each independent variable (metabolite) also change accordingly. Among them, the vertical axis indicates the value of the regression coefficient. When Log(λ) reaches a certain threshold, the regression coefficient gradually approaches 0 and finally reaches 0. Figure 2 In B, the horizontal axis is Log(λ), and the vertical axis is the target parameter (Deviance) when selecting the model by cross-validation, that is, -2 times the Log-likelihood. The smaller the value of the vertical axis, the better the fitting effect of the regression equation. For each value of λ, an effective parameter estimation range can be obtained. When the value of λ reaches the lowest point, the left dotted line indicates that the fitting ability of the model reaches the optimal. In the range of λ-min, the optimal value of the model is obtained by calculating the minimum mean square error. The top number is the number of remaining variables (metabolites) in the equation when λ takes different values. Referring to λ-min, 10 metabolites with non-zero regression coefficients in amniotic fluid were retained.

[0043] Random forest method

[0044] Using the 34 amniotic fluid differential metabolites as input variables, the random forest algorithm in the "randomForest" package of R language software was used to determine the most important features in the model. The Mean Decrease Accuracy (MDA) refers to the degree of decrease in the prediction accuracy of the random forest when the value of a certain variable becomes a random number. The larger its value, the greater the impact of the variable on the prediction accuracy of the model and the higher the importance of the variable. The average values of the mean decrease accuracy of each metabolite were averaged in 100 random forest repetitions. Finally, according to the importance ranking results of the variables (metabolites) analyzed by the random forest model, the top five amniotic fluid metabolites with greater impact on preterm birth were selected: Corydaline, Cytosine, Diethanolamine, Fenpropidin, Loureirinb; and it is considered to have good research value. See Figure 3, the bar graph on the left shows the average importance ranking of 100 random forest repeated results; the heat map on the right shows the average importance performance of each differential metabolite in each random forest, with the importance values distinguished by color, and the redder the color, the greater the importance value.

[0045] Based on the above analysis, the metabolites with the top five average accuracy reduction degrees in the LASSO regression results and the random forest importance ranking were overlapped and intersected, and presented in the form of a Venn diagram ( Figure 4 ).

[0046] Finally, the amniotic fluid metabolites Corydaline, Diethanolamine, and Loureirinb were selected as candidate biomarkers for predicting preterm birth. The specific information of the candidate biomarkers is shown in Table 2, and their specific expression is shown in Figure 5 , where * in the figure indicates P<0.05 and ** indicates P<0.001; P is the preterm birth group, NP is the full-term birth group, and FC is preterm / full-term birth.

[0047] Table 2 Expression information table of amniotic fluid candidate metabolic markers

[0048]

[0049] 1.1 Preterm birth prediction model

[0050] The candidate metabolic markers were used for model fitting to confirm their relationship with preterm birth. The dependent variable was preterm birth (1 = yes, 0 = no), and the independent variables included the candidate metabolic markers. The results of the Logistic regression model showed that the metabolites Corydaline (OR = 2.39, 95% CI: 1.07 - 3.83, P = 0.019) and Loureirinb (OR = 2.45, 95% CI: 1.73 - 4.09, P = 0.039) with low expression levels in amniotic fluid were risk factors for preterm birth, and Diethanolamine (OR = 0.45, 95% CI: 0.19 - 1.31, P = 0.024) was a protective factor for preterm birth. Then, the logistic regression model was presented in the form of a nomogram. A nomogram integrates multiple prediction indicators and uses a scaled line segment to be drawn on the same plane according to a certain proportion to express the mutual relationship between various variables in the prediction model. According to the contribution degree (the magnitude of the regression coefficient) of each influencing factor in the model to the outcome variable, scores are assigned to each value level of each influencing factor, and then the scores are added together to obtain the total score. Finally, through the functional conversion relationship between the total score and the occurrence probability of the outcome event, the predicted value of the outcome event of this individual is calculated.

[0051] Such as Figure 6As shown, the expression value of Corydaline in amniotic fluid is 20.6, the expression value of Diethanolamin is 22.5, and the expression value of Loureirinb is 19.6. The total score of their corresponding scores is 168, and the corresponding OR value is 1.25.

[0052] 1.2 Model performance evaluation

[0053] 1.2.1 ROC curve

[0054] The ROC curve (Receiver Operating Characteristic curve), that is, the receiver operating characteristic curve, is mainly used to evaluate the classification / prediction ability of a continuous index for a binary outcome, and to find the optimal index cut-off point (Cut-off point) to make the classification effect of the index optimal. AUC (Area Under Curve) is the area under the ROC curve. The ROC curve was used to evaluate the diagnostic ability of the above-mentioned candidate metabolic markers. The results showed that the AUC of the candidate metabolic markers was all > 0.70. The specific results are shown in Table 3.

[0055] Table 3 ROC diagnostic efficacy table of candidate metabolic markers

[0056]

[0057] The efficacy of a single candidate metabolic marker in predicting preterm birth was analyzed, and the results are shown in Figure 7 . The AUCs of the candidate metabolites Corydaline, Diethanolamine, and Loureirinb in amniotic fluid for predicting preterm birth were 0.82 (95% CI: 0.68 - 0.96), 0.72 (95% CI: 0.52 - 0.92), and 0.74 (95% CI: 0.56 - 0.92), respectively.

[0058] A joint prediction model of multiple candidate markers was fitted by logistic regression. The AUC of the joint marker prediction model composed of amniotic fluid metabolites (Corydaline + Diethanolamine + Loureirin b) reached 0.88 (95% CI: 0.78 - 0.96). The cut-off value of the model was 0.68, the sensitivity was 0.89, and the specificity was 0.68. The results of the Delong test showed that the joint marker prediction model composed of amniotic fluid metabolites (Corydaline + Diethanolamine + Loureirin b) was superior to the prediction models with Corydaline, Diethanolamine, and Loureirin b as single markers, and the difference was statistically significant (P1 = 0.033, P2 = 0.010, P3 = 0.013).

[0059] The results showed that the model constructed by the joint metabolic markers in amniotic fluid was the best choice for differentiating preterm pregnant women from term pregnant women.

[0060] Discrimination is an index to measure the ability of a model to distinguish different categories (such as healthy and diseased, survival and death). An excellent model can effectively identify the differences between the event group and the non-event group and achieve optimal risk management. The most commonly used indexes to measure discrimination include ROC and C-Statistic (also known as C-Index, C-Index). The value range of C-Index is 0 - 1. The closer the value is to 1, the better the performance of the model in correct classification, and the higher the accuracy and reliability of the model. The endpoint event of this study was a binary variable of whether preterm birth occurred. Therefore, the C-Index of the model was the same as the AUC. The C-Index value of the preterm amniotic fluid metabolite marker model was 0.877, indicating a relatively high discrimination.

[0061] Calibration reflects the accuracy of the model, which can be evaluated by plotting the consistency prediction risk curve to better demonstrate the reliability and practicality of the model. See Figure 8, the vertical axis represents the probability of actual preterm birth, and the horizontal axis represents the predicted probability of preterm birth given by the model. By using the diagonal gray line to depict the ideal model, its accuracy can be seen. The dotted line represents the performance of this combined marker model. If the degree of fit between the two images is high, it indicates that the model has high accuracy. The vertical line at the bottom of the figure represents the magnitude and direction of the calibration error. The longer the vertical line, the greater the error at that predicted probability. The Brier score measures the calibration of the model in a quantitative way, and the closer its value is to 0, the better the calibration of the model. The results show that the Brier score of the amniotic fluid metabolic marker preterm birth prediction model is 0.142, and the P values (S: p) of the model are all > 0.05, indicating that through the calibration test, the calibration of the model is good.

[0062] Precision, also known as the Positive Predictive Value (PPV), is an important indicator for evaluating the performance of a classification model in positive class prediction in a prediction model. Precision measures the proportion of cases that are actually positive among all cases predicted as positive by the model, that is, a high precision means a lower false positive rate of the model. In this study, the precision of the amniotic fluid metabolic marker preterm birth prediction model was 0.815.

[0063] The F1 Score is a comprehensive performance indicator in a prediction model. It takes into account both precision and recall and is a balanced measure of the two, especially often used to measure the precision and robustness of the model when the data class is imbalanced. In this study, the F1 score of the amniotic fluid metabolic marker preterm birth prediction model was 0.846.

[0064] 1.3 Model Validation (Bootstrap Validation)

[0065] Through the Bootstrap Validation method, the development cohort of the model can be used for random sampling with replacement to create a resampled set with the same data volume, which is then used to train the model and then used as the validation set to evaluate the performance of the model. After N iterations, the performance of the model in internal validation can finally be obtained. The Bootstrap validation method is a powerful statistical resampling technique used to estimate the characteristics of the sample distribution. By repeatedly randomly sampling from the original dataset with replacement, multiple virtual sample sets (called Bootstrap samples) are created, and the statistical results of all Bootstrap samples are aggregated to evaluate the performance of the model (confidence intervals of parameters, distribution of model accuracy, etc.), and then the model is validated.

[0066] In this study, the Bootstrap resampling method (100 times) was used for internal model validation. The results showed that the average AUC value of the combined amniotic fluid metabolic marker prediction model was 0.89 (95% CI: 0.86 - 0.93), the average C-Index was 0.89 (95% CI: 0.86 - 0.93), and the sensitivity and specificity were 0.89 and 0.66, respectively.

Claims

1. Use of a marker for predicting the risk of premature birth in the preparation of a reagent for predicting or evaluating the risk of premature birth, characterized in that: The marker is one or more of Corydaline, Diethanolamine and Loureirin b.

2. The use according to claim 1, characterized in that: The reagent is a detection reagent for detecting the expression amount of biomarkers associated with the risk of premature birth in the subject's sample.

3. The use according to claim 1, characterized in that: The subject sample is the amniotic fluid of a pregnant subject.

4. A kit comprising specific antibodies or mass spectrometry detection reagents for detecting corydaline, diethanolamine, and Loureirinb.

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