Polycystic ovarian syndrome pregnancy outcome diagnosis marker and application thereof

Abstract

The invention relates to the field of biomarkers, and particularly provides a polycystic ovarian syndrome pregnancy outcome diagnosis marker and application thereof. According to the present invention, the endometrial targeting metabonomics research is adopted to screen the disease-related potential metabolic markers such as N-Acetylaspartic acid, Suberic acid and isoallolithocholic acid, and the three metabolites are subjected to quantitative detection so as to accurately predict the pregnancy outcome of the polycystic ovarian syndrome patient, such that the pregnancy outcome of the polycystic ovarian syndrome patient can be accurately predicted, and the prediction of the pregnancy outcome of the polycystic ovarian syndrome patient can be easily achieved, such that the disease-related potential metabolic markers such as N-Acetylaspartic acid, suberic acid and isoallolithocholic acid can be provided. A new path is provided for early intervention of the disease state to improve the pregnancy outcome, and a new drug target is provided for evaluation of the endometrial function of the polycystic ovarian syndrome.

Description

Technical Field

[0001] The present invention relates to the field of biomarkers. Specifically, the present invention relates to a diagnostic marker for pregnancy outcome in polycystic ovary syndrome and its application. Background Art

[0002] Polycystic ovary syndrome (PCOS) is a complex and heterogeneous endocrine and metabolic disorder syndrome, and is the main cause of anovulatory infertility in women of childbearing age, accounting for more than 80%. Due to racial specificity and inconsistent diagnostic criteria, the reported incidence of PCOS in various countries is 5-10%. Previous studies have shown that PCOS patients have an increased risk of adverse pregnancy outcomes, manifested as increased rates of spontaneous abortion and implantation failure, and decreased rates of live birth and clinical pregnancy. After adjusting for various confounding factors such as maternal age, body mass index, number and quality of transferred embryos, the abortion rate and very preterm birth rate in PCOS patients are still relatively high. Therefore, endometrial dysfunction may be one of the potential causes of adverse pregnancy outcomes in PCOS.

[0003] Under physiological conditions, the endometrium undergoes cyclic changes under the sequential action of estrogen and progesterone, and is divided into the proliferative phase, secretory phase and menstrual phase. The transformation of the endometrium from the proliferative phase to the secretory phase is the primary condition for fertilized egg implantation. In PCOS patients, due to the continuous stimulation of high estrogen, hyperandrogenemia and progesterone resistance, the proliferative phase is prolonged, leading to an increased risk of endometrial cancer. Therefore, intervening in the function of the PCOS endometrium at an earlier stage of the menstrual cycle, that is, the proliferative phase, helps to restore the normal cyclic rhythm of the patient and provides an important prerequisite for successful pregnancy in the later stage and reduction of pregnancy complications.

[0004] As the most downstream event of the biological system, the changes in metabolites can reflect the upstream disorders or the final results directly derived from environmental factors, and reveal the actual situation in the body. Therefore, by discovering new and reliable diagnostic markers through metabolomics, more targeted intervention strategies for improving the pregnancy outcome of PCOS can be found. However, there is currently no study revealing the local metabolic changes in the PCOS endometrium. Summary of the Invention

[0005] In order to fill the gaps in the existing technology, the present invention provides a diagnostic marker for pregnancy outcome in polycystic ovary syndrome and its application. Specifically, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a combination of metabolic markers for diagnosing pregnancy outcomes in polycystic ovary syndrome (PCOS), and the combination of metabolic markers includes the following three metabolites: N-acetylaspartic acid, suberic acid, and isoalloLCA.

[0007] In a second aspect, the present invention provides a prediction model for pregnancy outcomes in polycystic ovary syndrome (PCOS), characterized in that the model is established based on the aforementioned combination of metabolic markers through binary logistic regression analysis and is used to predict whether a clinical pregnancy occurs in PCOS patients.

[0008] In one embodiment, the logistic regression equation of the model is:

[0009] Logit(P) = 3.433 + 0.045 × level of N-acetylaspartic acid - 5.256 × level of suberic acid + 1.326 × level of isoalloLCA where P is the predicted probability of a clinical pregnancy occurring in PCOS patients.

[0010] In one embodiment, the level refers to the expression level of the metabolite in the endometrium.

[0011] In a third aspect, the present invention provides a kit for diagnosing pregnancy outcomes in polycystic ovary syndrome (PCOS), and the kit contains reagents for detecting the expression levels of the aforementioned combination of metabolic markers.

[0012] In one embodiment, the kit quantitatively detects the metabolites by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology. Description of the Drawings

[0013] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0014] Figure 1 is the targeted metabolic profile of the endometrial extract;

[0015] Figure 2 is the graph of 38 metabolites with significant differences in the proliferative-phase endometrium of PCOS in the examples of the present invention;

[0016] Figure 3 is the orthogonal partial least squares discriminant analysis graph in the examples of the present invention;

[0017] Figure 4ROC analysis of specific metabolic markers for the diagnostic ability of clinical pregnancy rate in the embodiments of the present invention (discovery set);

[0018] Figure 5 ROC analysis of specific metabolic markers for the diagnostic ability of clinical pregnancy rate in the embodiments of the present invention (validation set). Detailed implementation manners

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0020] Example 1 Screening of biomarkers

[0021] 1. Research subjects and sample collection

[0022] The research was approved by the Ethics Committee of Peking University Third Hospital, and female patients who visited the Reproductive Medicine Center of Peking University Third Hospital from 2022 to 2024 were recruited.

[0023] 1.1 Inclusion and exclusion criteria:

[0024] PCOS group: According to the Rotterdam PCOS diagnostic criteria in 2003, the enrolled population was selected, and two of the following three conditions were required: oligomenorrhea and / or amenorrhea, hyperandrogenism or clinical manifestations of hyperandrogenism (hirsutism, acne, etc.), polycystic ovarian changes under ultrasound; at the same time, other causes of hyperandrogenism were excluded.

[0025] Control group: Patients who sought treatment due to bilateral fallopian tube obstruction or due to male factor alone, with regular menstrual cycles and no manifestations of hyperandrogenism and polycystic ovaries;

[0026] Exclusion criteria: Pregnancy, endocrine and metabolic related diseases (congenital adrenal hyperplasia, androgen-secreting tumors, hypogonadotropic hypogonadism, thyroid dysfunction, Cushing's syndrome, hyperprolactinemia and premature ovarian failure), ovarian cysts or tumors, and chromosomal abnormalities.

[0027] All selected subjects had no history of taking hormones, insulin sensitizers, and lipid-lowering drugs in the past 3 months.

[0028] 1.2 Detection indexes of enrolled patients:

[0029] Basic indexes: Age, height, weight, etc.;

[0030] Endocrine and metabolic indexes: Blood samples were taken on the 3rd to 5th day of menstruation to detect sex hormone levels (follicle-stimulating hormone, luteinizing hormone, estrogen, total testosterone, androstenedione), anti-Müllerian hormone; fasting blood glucose, blood lipids (cholesterol, triglyceride, low-density lipoprotein, high-density lipoprotein), etc.;

[0031] B-ultrasound examination: Perform an ultrasound examination to measure the number and volume of ovarian follicles 3 to 7 days after menstruation has ended (for women with sexual experience, a transvaginal B-ultrasound is used; for women without sexual experience, a transabdominal or transrectal B-ultrasound is used) to determine the antral follicle count (AFC).

[0032] By screening the population that met the inclusion criteria, a total of 140 cases were finally included, including 70 PCOS patients and 70 control subjects. They were randomly divided into a discovery set and a validation set, with each set including 35 PCOS patients and 35 control subjects. The basic characteristics and clinical test indicators of the two groups, the PCOS group and the control group, are shown in Table 1, and the results are consistent with the clinical phenotype characteristics of PCOS patients. There were no significant differences in age, follicle-stimulating hormone, estrogen, progesterone, fasting blood glucose, and low-density cholesterol between the two groups; in the PCOS group, the endocrine and lipid-related indicators were abnormal, specifically, luteinizing hormone (LH), androgen (total testosterone, androstenedione), anti-Müllerian hormone (AMH), antral follicle count (AFC), cholesterol, triglyceride, and high-density lipoprotein were significantly higher than those in the control group.

[0033] Table 1: Comparison of clinical test indicators between PCOS and the control group

[0034]

[0035]

[0036] Data representation: Data are presented as mean ± standard deviation.

[0037] a The independent samples t-test was used to evaluate the differences between the PCOS group and the control group.

[0038] b The Mann-Whitney U test was used to evaluate the differences between the PCOS group and the control group.

[0039] 2. Targeted metabolomics detection

[0040] Endometrial tissue was obtained from discarded samples during hysteroscopic surgery. Samples with a pathological examination report indicating the proliferative phase were included in subsequent tests. The targeted metabolic profiles of endometrial extracts were analyzed by Metabo Profile (Shanghai, China), and an ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) system (ACQUITY UPLC Xevo TQ-S, Waters Corp., Milford, MA, USA) was used to quantify all targeted metabolites ( Figure 1)。The raw data files generated by UPLC-MS / MS were processed using TMBQ software (v1.0, Metabo Profile, Shanghai, China) to perform peak integration, calibration, and quantification for each metabolite. The variable importance in the projection (VIP) values of each variable in the OPLS-DA model was calculated to evaluate its contribution to classification. Student's t-tests were performed to determine the significance of differences between two independent sample groups. VIP > 1 and p-value < 0.05 were used to screen for significantly changed metabolites, and a total of 38 metabolic markers were obtained ( Figure 2 )。

[0041] Orthogonal partial least squares discriminant analysis (OPLS-DA) is a new type of multivariate statistical method first proposed by Johan Tryggde et al. in 2002. The biggest feature of OPLS-DA is that it can filter out the data variation in the independent variables that is irrelevant to the classification variable, making the classification information mainly concentrated in one principal component, thus making the model simple and easy to interpret, and improving the analytical ability and effectiveness of the model. We used the OPLS-DA method to establish a model between the differential metabolic profiles of the proliferative endometrium and the PCOS disease grouping relationship. It can be seen from the score plot that this model can well distinguish PCOS from the control group ( Figure 3 )。

[0042] 3. Screening of metabolic markers for predicting PCOS pregnancy outcomes

[0043] Through the above multivariate statistical analysis, 4 markers with VIP less than 1 calculated by the OPLS-DA model were removed, and we screened out 34 metabolic markers related to PCOS endometrial function. Receiver operating characteristic (ROC) curve analysis was applied to detect the diagnostic ability of each amino acid marker for the clinical pregnancy rate. We additionally selected endometrial tissues from 61 clinically pregnant patients and 45 non-clinically pregnant patients (from the Reproductive Medicine Center of Peking University Third Hospital, and all patients gave informed consent), and analyzed them after detecting the above 34 metabolites. The combined area under the curve (AUC) of 3 metabolites (N-acetylaspartic acid, suberic acid, isoalloLCA) was > 0.8 and P < 0.001, indicating good diagnostic ability ( Figure 4 )。

[0044] Example 2 Establishment of a model for predicting PCOS pregnancy outcomes

[0045] A prediction model for PCOS pregnancy outcomes was established using the above three selected amino acids. First, binary Logistic regression analysis was applied, and all the above three amino acid markers were included in the model variables. The enter method was selected for the analysis method, and the predicted probability of clinical pregnancy in PCOS subjects was calculated. The comprehensive test P value of the Logistic regression analysis model coefficients was <0.001, indicating that the model was generally significant; the Hosmer and Lemeshow Test (testing the goodness of fit of the model) P = 0.170, indicating that the information in the current data had been fully extracted and the model had a high goodness of fit. Table 2 lists the amino acids and their parameters in this model (regression equation).

[0046] Table 2: Logistic regression analysis of clinical pregnancy based on three metabolic markers

[0047]

[0048] According to the above regression results, the score Logit(P) for predicting whether clinical pregnancy occurs for each subject based on the levels of these three metabolites can be written as follows:

[0049] Logit(P) = 3.433 + 0.045 * N-Acetyaspartic acid level – 5.256 * Suberic acid level + 1.326 * isoalloLCA level

[0050] And the predicted probability of clinical pregnancy for each PCOS subject can be calculated according to the following formula:

[0051]

[0052] Example 3 Evaluation of the efficacy of the diagnostic model

[0053] So far, we can evaluate the accuracy of predicting whether PCOS individuals will have clinical pregnancy based on the levels of the above three metabolites according to the true disease status and predicted probability of the subjects. The area under the receiver operating characteristic (ROC) curve (AUC) was used to determine the diagnostic ability of this group of metabolites for the occurrence of clinical pregnancy. The analysis showed that the area under the ROC curve of the combined diagnostic test of these three metabolites in this group was 0.872 (0.807 - 0.936), with a sensitivity of 77.0% and a specificity of 82.2% ( Figure 4 ). It shows that the quantitative detection of the levels of three metabolites (N-acetylaspartic acid, suberic acid, and isoalloLCA) in the proliferative endometrium can early predict whether PCOS patients will have clinical pregnancy, and has good diagnostic sensitivity and accuracy.

[0054] To further verify the effectiveness of the diagnostic model in clinical practice, we used 35 PCOS patients and 35 controls in the validation set to re-determine the diagnostic ability of the prediction model through the area under the receiver operating characteristic (ROC) curve (AUC). The analysis showed that the area under the ROC curve of the combined diagnostic test of the three metabolites in this model was 0.813, with a sensitivity of 76.7% and a specificity of 81.3% ( Figure 5 ).

[0055] Therefore, N-acetylaspartic acid, suberic acid, and isoalloLCA can not only serve as important indicators for early evaluation of the endometrial function in PCOS, but also provide early intervention targets for preventing adverse pregnancy outcomes in PCOS patients from the perspective of metabolic abnormalities.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and its equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A metabolic marker combination for diagnosing pregnancy outcomes in polycystic ovary syndrome (PCOS), characterized in that: The metabolic marker combination includes the following three metabolites: N-acetylaspartic acid, suberic acid, and isoalloLCA.

2. A prediction model for pregnancy outcome in polycystic ovary syndrome (PCOS), characterized in that: The model is based on the metabolic marker combination described in claim 1 and is established through binary logistic regression analysis to predict whether clinical pregnancy occurs in PCOS patients.

3. The prediction model according to claim 2, characterized in that The Logistic regression equation of the model is: Logit(P)=3.433+0.045×N-acetylaspartic acid level-5.256×suberic acid level+1.326×isoaltolithocholic acid level, where P is the predicted probability of clinical pregnancy in PCOS patients.

4. The prediction model according to claim 3, characterized in that The levels refer to the expression levels of metabolites in the endometrium.

5. A kit for diagnosing pregnancy outcomes in polycystic ovary syndrome (PCOS), characterized in that: The kit comprises reagents for detecting the expression level of the metabolic marker combination according to claim 1.

6. The kit according to claim 5, characterized in that The kit performs quantitative detection of metabolites by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) technology.

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