Application of biomarker combination in preparation of kit for predicting ovarian function related indexes

Abstract

The invention provides application of a biomarker combination in preparation of a kit for predicting ovarian function related indexes, and belongs to the field of reproductive endocrine. The ovarian function related indexes comprise the egg obtaining number and the embryo cleavage number; the biomarkers comprise a first biomarker and a second biomarker; the first biomarker is used for predicting the egg obtaining number; the second biomarker is used for predicting the number of embryo cleavage; the first biomarker is prepared from at least one of histidine, methionine and trans-4-hydroxyproline; and the second biomarker is prepared from at least one of histidine, trans-4-hydroxyproline, choline, N2, N2-dimethyl guanosine and gamma-glutamyl valine. The biomarker provided by the invention can more objectively and accurately predict the egg obtaining number and the embryo cleavage number. Based on the accurate prediction, a doctor can know the ovarian function and the oocyte development potential of the patient in advance, so that a proper treatment strategy is formulated for the patient, and the success rate of the assisted reproduction technology is greatly improved.

Description

Technical Field

[0001] The present application belongs to the field of reproductive endocrinology technology, and specifically relates to the application of a biomarker combination in the preparation of a kit for predicting ovarian function-related indicators. Background Art

[0002] In the field of reproductive medicine, women's fertility is closely related to ovarian function. Ovarian aging often leads to reproductive disorders, and assisted reproductive technology (ART) has become an important hope for many infertile patients. However, there are many challenges in evaluating ovarian function and predicting ART outcomes. Age is a key factor affecting women's reproductive ability. With age, reproductive ability declines significantly, such as a decrease in the primordial follicle pool and a decrease in the responsiveness of the ovaries to ovulation induction. The number of eggs retrieved, the fertilization rate, the high-quality embryo rate, the embryo implantation rate, and the clinical pregnancy rate are all affected. However, age is only a rough indicator and cannot accurately quantify the complex physiological changes of the ovaries and the developmental potential of oocytes. The outcomes of ART treatment for many women of similar age vary greatly, highlighting the inaccuracy of age assessment alone, and more accurate assessment methods are urgently needed. Commonly used ovarian reserve assessment indicators in clinical practice, such as basal antral follicle count (AFC) and anti-Mullerian hormone (AMH), can provide certain information, but each has its limitations. AFC is affected by the subjective factors of the ultrasound examiner, and only reflects the number of antral follicles, but cannot reflect the quality and function of follicles; although AMH is relatively stable, it is affected by diseases or drugs, and mainly focuses on the evaluation of ovarian reserve function, which cannot fully reflect ovarian function and oocyte quality, and it is difficult to meet the needs of clinical accurate evaluation. In addition, the morphological methods commonly used to evaluate the developmental potential of oocytes are mainly based on the morphology of the cumulus oocyte complex (COCs), the morphology of the first polar body, the size of the perivitelline space, the cytoplasmic morphology, and the spindle morphology. Although the morphological method is non-invasive and convenient, it lacks a unified standard and cannot accurately reflect the functional state of the oocyte. It is greatly affected by the subjective influence of the observer. The differences in subjective judgments of different observers lead to different evaluation results of the association between oocyte and embryonic development, and the accuracy and reliability are limited. In view of the many shortcomings of the existing evaluation methods, it is urgent to find more objective and accurate methods to evaluate indicators related to oocyte quality and ovarian function, which is of great significance to improve the success rate of ART and improve the reproductive outcomes of infertile patients. Summary of the invention

[0003] In view of this, the present application provides an application of a biomarker for more objectively and accurately evaluating ovarian function-related indicators in the preparation of a product for predicting ovarian function-related indicators. The application can more objectively and accurately predict the number of eggs retrieved and the number of embryo cleavages. Based on these precise predictions, doctors can understand the patient's ovarian function status and oocyte development potential in advance, so as to formulate appropriate treatment strategies according to the individual patient's situation, greatly improve the success rate of assisted reproductive technology, and ultimately help infertile patients realize their dream of having children, improve their reproductive outcomes, and bring new breakthroughs and hope to the field of reproductive medicine.

[0004] In a first aspect, the present application provides an application of a biomarker in the preparation of a product for predicting ovarian function-related indicators, wherein the ovarian function-related indicators include the number of eggs retrieved and the number of embryo cleavages;

[0005] The biomarkers include a first biomarker and a second biomarker;

[0006] The first biomarker is used to predict the number of oocytes retrieved;

[0007] The second biomarker is used to predict the embryo cleavage number;

[0008] The first biomarker includes at least one of histidine, methionine, and trans-4-hydroxyproline;

[0009] The second biomarker includes at least one of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine and γ-glutamylvaline.

[0010] By adopting the above technical scheme, the present application can solve the current problem of lack of objective and accurate methods in evaluating ovarian function-related indicators (number of eggs obtained and number of embryonic cleavages). Existing evaluation methods such as age, basal antral follicle count (AFC), anti-Mullerian hormone (AMH) and morphological methods are limited, and it is impossible to accurately quantify the complex physiological changes of the ovaries and the developmental potential of oocytes, which is difficult to meet the needs of clinical accurate evaluation, affecting the success rate of assisted reproductive technology (ART) and the reproductive outcomes of infertile patients. The technical scheme provided in this application uses specific biomarkers (histidine, methionine and trans-4-hydroxyproline, etc. for predicting the number of eggs obtained, histidine, trans-4-hydroxyproline, choline, N2, N2-dimethylguanosine and γ-glutamylvaline, etc. for predicting the number of embryonic cleavages) to prepare related products, which can more objectively and accurately predict ovarian function-related indicators. This allows doctors to understand the patient's ovarian function status and oocyte development potential in advance, and thus formulate personalized treatment strategies accordingly, effectively overcoming the shortcomings of existing evaluation methods, greatly improving the success rate of ART, helping infertile patients realize their dreams of having children, bringing new breakthroughs to the field of reproductive medicine, and improving the reproductive outcomes of infertile patients.

[0011] Optionally, the first biomarker includes at least two of histidine, methionine and trans-4-hydroxyproline.

[0012] By adopting the above technical solution, this technical solution selects at least two of histidine, methionine and trans-4-hydroxyproline as the first biomarker. Compared with using only a single marker, it can reflect the relationship between ovarian function and the number of eggs obtained from multiple angles and levels. The combined use of multiple markers can increase the accuracy and stability of the prediction, reduce the impact of individual differences and experimental errors on the results, and more accurately predict the number of eggs obtained. This helps doctors to understand the potential egg-retrieving ability of the patient's ovaries more comprehensively and accurately before ART is implemented, so as to develop more targeted and personalized treatment plans for patients, improve the success rate of ART, better solve the fertility problems of infertile patients, fill the gaps in existing evaluation methods in accurately predicting the number of eggs obtained, and promote the further development of ovarian function assessment and infertility treatment in the field of reproductive medicine.

[0013] Optionally, the second biomarker comprises at least two of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine and γ-glutamylvaline.

[0014] Optionally, the first biomarker includes histidine, methionine and trans-4-hydroxyproline.

[0015] By adopting the above technical solution, this technical solution uses histidine, methionine and trans-4-hydroxyproline as the first biomarker to predict the number of eggs retrieved, which solves the problem of lack of accuracy of existing evaluation methods. These three markers establish connections with ovarian function and the number of eggs retrieved from different metabolic pathways or physiological processes, and their synergistic effect can more comprehensively and deeply reflect the physiological state and potential egg retrieval capacity of the ovaries. By comprehensively considering the levels of these three markers, the deviation caused by single-factor evaluation can be effectively reduced, and the accuracy and reliability of the prediction of the number of eggs retrieved can be significantly improved. This enables doctors to more accurately grasp the patient's ovarian function status before ART treatment, thereby tailoring a more optimized treatment plan for the patient, improving the success rate of ART, and increasing the chances of infertile patients realizing their desire to have children. It provides more effective technical support for the field of reproductive medicine in ovarian function assessment and infertility treatment, and promotes the development of this field in a more precise direction.

[0016] Optionally, the second biomarker comprises at least four of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine and γ-glutamylvaline.

[0017] By adopting the above technical scheme, in the field of reproductive medicine, accurate prediction of the number of embryo cleavages is crucial for the success of assisted reproductive technology (ART) and the improvement of reproductive outcomes of infertile patients. However, the existing evaluation methods, such as traditional ovarian reserve function indicators and oocyte morphological evaluation, are obviously insufficient in predicting the number of embryo cleavages, and cannot accurately reflect the potential of embryo cleavage, and it is difficult to meet the clinical demand for accurate evaluation, resulting in difficulty in formulating the most appropriate treatment strategy during the ART process. This technical scheme uses at least four of histidine, trans-4-hydroxyproline, choline, N2, N2-dimethylguanosine and γ-glutamylvaline as the second biomarker to predict the number of embryo cleavages, which effectively solves the problem of inaccuracy of existing evaluation methods. These multiple biomarkers cover different metabolic pathways or physiological processes, and can comprehensively reflect the biological information related to embryo cleavage from multiple dimensions. By jointly detecting at least four markers, they can complement and verify each other, reduce the problem of low accuracy caused by single marker detection, and significantly improve the accuracy and reliability of predicting the number of embryo cleavages. This enables doctors to more accurately understand the patient's embryonic cleavage potential in the early stages of ART treatment, so that they can develop a more scientific and reasonable treatment plan based on the patient's individual situation, improve the success rate of ART, bring greater fertility hope to infertile patients, and promote further development in the field of reproductive medicine in terms of embryo development potential assessment and infertility treatment technology, providing more powerful technical support for improving the fertility outcomes of infertile patients.

[0018] Optionally, the product further comprises an extraction reagent for extracting the biomarker from a biological sample; the biological sample is derived from the follicular fluid of a subject.

[0019] By adopting the above technical solution, this technical solution solves the problem of difficulty in obtaining biomarkers by including an extraction reagent for extracting biomarkers from follicular fluid in the product. As a microenvironment that is in direct contact with the oocyte, the biomarkers in the follicular fluid can accurately reflect the functional state of the ovary. The addition of the extraction reagent ensures that these biomarkers can be efficiently and stably extracted from the follicular fluid sample, ensuring the quality and representativeness of the test samples. This makes the subsequent prediction of the number of eggs obtained and the number of embryo cleavages based on these biomarkers more accurate and reliable, which helps doctors to more accurately understand the patient's ovarian function and oocyte development potential during ART treatment, and then formulate more personalized and effective treatment strategies, improve the success rate of ART, and improve the reproductive outcomes of infertile patients. It provides important technical guarantees for the accurate diagnosis and treatment in the field of reproductive medicine, and promotes the development of this field in ovarian function assessment and infertility treatment.

[0020] Optionally, the product also includes a tool component for collecting, storing and preprocessing follicular fluid samples.

[0021] The tool assembly includes a laminar flow hood, a low-temperature refrigerated centrifuge, centrifuge tubes, and an ice maker.

[0022] Optionally, the content of the first biomarker is negatively correlated with the number of retrieved oocytes in the biological sample; the content of the second biomarker is negatively correlated with the number of embryo cleavages in the biological sample.

[0023] By adopting the above technical solution, in the field of reproductive medicine, accurately evaluating ovarian function-related indicators (number of retrieved oocytes and number of embryo cleavages) is crucial for the successful implementation of assisted reproductive technology (ART) and the improvement of the fertility outcomes of infertile patients. However, existing evaluation methods lack sufficient accuracy and reliability in predicting the number of retrieved oocytes and the number of embryo cleavages, and it is difficult to effectively guide clinical treatment decisions. In this technical solution, the content of the first biomarker is negatively correlated with the number of retrieved oocytes in the biological sample (follicular fluid), and the content of the second biomarker is negatively correlated with the number of embryo cleavages in the biological sample. This characteristic solves the problem that existing evaluation methods cannot accurately reflect the relationship between ovarian function and the number of retrieved oocytes and the number of embryo cleavages. By detecting the content of these biomarkers in follicular fluid, doctors can utilize their negative correlation with the number of retrieved oocytes and the number of embryo cleavages to more accurately predict the oocyte retrieval situation and embryo cleavage potential of patients during ART treatment. This helps to pre-evaluate the ovarian function status of patients before treatment, so as to formulate personalized and optimized treatment plans according to the prediction results, such as adjusting the dosage and usage time of ovulation induction drugs, selecting the appropriate embryo transfer timing, etc. Ultimately, it improves the success rate of ART, increases the chance of successful pregnancy of infertile patients, improves their fertility outcomes, provides a more scientific and effective technical means for the field of reproductive medicine in ovarian function evaluation and infertility treatment, and promotes the development of this field.

[0024] This application has at least the following beneficial technical effects:

[0025] This application can solve the current problem of lack of objective and accurate methods in evaluating ovarian function-related indicators (number of eggs obtained and number of embryonic cleavages). Existing evaluation methods such as age, basal antral follicle count (AFC), anti-Mullerian hormone (AMH) and morphological methods are limited and cannot accurately quantify the complex physiological changes of the ovaries and the developmental potential of oocytes. It is difficult to meet the needs of clinical accurate evaluation, affecting the success rate of assisted reproductive technology (ART) and the reproductive outcomes of infertile patients. The technical solution provided in this application uses specific biomarkers (histidine, methionine and trans-4-hydroxyproline, etc. for predicting the number of eggs obtained, histidine, trans-4-hydroxyproline, choline, N2, N2-dimethylguanosine and γ-glutamylvaline, etc. for predicting the number of embryonic cleavages) to prepare related products, which can more objectively and accurately predict ovarian function-related indicators. This allows doctors to understand the patient's ovarian function status and oocyte development potential in advance, and thus formulate personalized treatment strategies accordingly, effectively overcoming the shortcomings of existing evaluation methods, greatly improving the success rate of ART, helping infertile patients realize their dreams of having children, bringing new breakthroughs to the field of reproductive medicine, and improving the reproductive outcomes of infertile patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Flow chart of the mass spectrometry sample preparation technique provided for a specific embodiment of the present application; a small portion of each study sample (colored cylinder) is pooled to create a replicate sample (multi-colored cylinder), which is then injected periodically throughout the process; for studies spanning multiple days, a data normalization step is performed to correct for variations caused by instrument tuning differences between days; variability between consistently tested biochemicals is used to calculate an estimate of overall process variability;

[0027] Figure 2 The principal component analysis (PCA) diagram, heat map and metabolite levels of different groups provided for the specific implementation of the present application; (A), PCA diagram and 2D cluster diagram describing the separation trend of the two groups, red dots and green dots represent elderly and young women, respectively; (B), the levels of each metabolite in the elderly group and the young control group were tested using an unpaired Student's t-test, and then the p-value of the t-test was adjusted using the FDR method to obtain the q-value considering multiple comparisons, and the Foldchange was calculated as the ratio of the mean metabolite of the elderly group to that of the young control group; (C), heat map of hierarchical cluster analysis, the standardized levels of metabolites are represented by different colors;

[0028] Figure 3Differential metabolites in follicular fluid of the advanced age group and the control group provided for the specific embodiments of the present application, and their correlation with maternal age, number of retrieved oocytes and number of cleavage embryos; (A), comparison of selected metabolites in follicular fluid of the advanced age group and the young control group (t test); (B), correlation between metabolites and age (Pearson correlation test), (C), correlation between metabolites and number of retrieved oocytes (Pearson correlation test); (D), correlation between metabolites and number of cleavage embryos (Pearson correlation test);

[0029] Figure 4 Schematic diagram of the changes in metabolites in follicular fluid with age. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the present application more clear, the present application is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.

[0031] In the field of reproductive medicine, women's fertility is closely related to ovarian function. Ovarian aging often leads to reproductive disorders, and assisted reproductive technology (ART) has become an important hope for many infertile patients. However, there are currently many challenges in assessing ovarian function and predicting ART outcomes. Age is a key factor affecting women's reproductive ability. With age, reproductive ability declines significantly, such as a decrease in the primordial follicle pool and a decrease in the responsiveness of the ovaries to ovulation induction. The number of eggs retrieved, the fertilization rate, the high-quality embryo rate, the embryo implantation rate, and the clinical pregnancy rate are all affected. However, age is only a rough indicator and cannot accurately quantify the complex physiological changes of the ovaries and the developmental potential of oocytes. The outcomes of ART treatment for many women of similar age vary greatly, highlighting the inaccuracy of age assessment alone, and more accurate assessment methods are urgently needed. Although the commonly used ovarian reserve assessment indicators in clinical practice, such as basal antral follicle count (AFC) and anti-Mullerian hormone (AMH), can provide certain information, they each have their limitations. AFC is affected by the subjective factors of the ultrasound examiner, and only reflects the number of antral follicles, but cannot reflect the quality and function of follicles; although AMH is relatively stable, it is affected by diseases or drugs, and mainly focuses on the evaluation of ovarian reserve function, which cannot fully reflect ovarian function and oocyte quality, and it is difficult to meet the needs of clinical accurate evaluation. In addition, the morphological methods commonly used to evaluate the developmental potential of oocytes are mainly based on the morphology of the cumulus oocyte complex (COCs), the morphology of the first polar body, the size of the perivitelline space, the cytoplasmic morphology, and the spindle morphology. Although the morphological method is non-invasive and convenient, it lacks a unified standard and cannot accurately reflect the functional state of the oocyte. It is greatly affected by the subjective influence of the observer. The differences in subjective judgments of different observers lead to different evaluation results of the association between oocyte and embryonic development, and the accuracy and reliability are limited. In view of the many shortcomings of the existing evaluation methods, it is urgent to find more objective and accurate methods to evaluate indicators related to oocyte quality and ovarian function, which is of great significance to improve the success rate of ART and improve the reproductive outcomes of infertile patients.

[0032] In view of this, the present application provides a kit for more objectively and accurately evaluating ovarian function-related indicators. The kit uses biomarkers such as histidine, methionine, trans-4-hydroxyproline, choline, N2, N2-dimethylguanosine and γ-glutamylvaline to more objectively and accurately predict the number of eggs obtained and the number of embryo cleavages. Based on these accurate predictions, doctors can understand the patient's ovarian function status and oocyte development potential in advance, so as to formulate appropriate treatment strategies according to the individual patient's situation, greatly improve the success rate of assisted reproductive technology, and ultimately help infertile patients realize their dream of having children, improve their reproductive outcomes, and bring new breakthroughs and hopes to the field of reproductive medicine.

[0033] In a first aspect, the present application provides an application of a biomarker in the preparation of a product for predicting ovarian function-related indicators, wherein the ovarian function-related indicators include the number of eggs retrieved and the number of embryo cleavages;

[0034] The biomarker includes a first biomarker and a second biomarker; the first biomarker is used to predict the number of retrieved eggs; the second biomarker is used to predict the number of embryo cleavages;

[0035] The first biomarker includes at least one of histidine, methionine, and trans-4-hydroxyproline;

[0036] The second biomarker includes at least one of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine, and gamma-glutamylvaline.

[0037] The innovation of this application is that this application can solve the current problem of lack of objective and accurate methods in evaluating ovarian function-related indicators (number of eggs obtained and number of embryonic cleavages). Existing evaluation methods such as age, basal antral follicle count (AFC), anti-Mullerian hormone (AMH) and morphological methods have limitations, and cannot accurately quantify the complex physiological changes of the ovaries and the developmental potential of oocytes. It is difficult to meet the needs of clinical accurate evaluation, affecting the success rate of assisted reproductive technology (ART) and the reproductive outcomes of infertile patients. The technical solution provided by this application uses specific biomarkers (histidine, methionine and trans-4-hydroxyproline, etc. for predicting the number of eggs obtained, histidine, trans-4-hydroxyproline, choline, N2, N2-dimethylguanosine and γ-glutamylvaline, etc. for predicting the number of embryonic cleavages) to prepare related products, which can more objectively and accurately predict ovarian function-related indicators. This allows doctors to understand the patient's ovarian function status and oocyte development potential in advance, and thus formulate personalized treatment strategies accordingly, effectively overcoming the shortcomings of existing evaluation methods, greatly improving the success rate of ART, helping infertile patients realize their dreams of having children, bringing new breakthroughs to the field of reproductive medicine, and improving the reproductive outcomes of infertile patients.

[0038] As female fertility declines with age, this natural change reaches its peak during reproductive aging. Human follicular fluid is rich in low molecular weight metabolites responsible for oocyte maturation. Metabolomics approaches are powerful tools to investigate biochemical markers of oocyte quality in follicular fluid. It is necessary to identify and quantify reliable metabolites in follicular fluid that reflect the developmental potential of oocytes.

[0039] Therefore, this application recruited a total of 30 women who received ART treatment, collected follicular fluid from the subjects, used liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) to determine the levels and metabolic significance of 311 metabolites in the follicular fluid, and performed quantitative metabolomics analysis. The metabolomics study of follicular fluid revealed that 70 metabolites were significantly different between the elderly group and the control group at the level of small molecule metabolites. Eight small molecule metabolites in follicular fluid were significantly correlated with age, of which three small molecule metabolites were significantly correlated with the number of eggs retrieved, and another five small molecule metabolites were significantly correlated with the number of embryonic cleavages.

[0040] This application discovered a method for analyzing the relationship between follicular fluid metabolites and the number of retrieved eggs and the number of embryo cleavages, and identified differential metabolites in the follicular fluid of the advanced-age group and the control group. These small molecule metabolites may be potential biomarkers for oocyte maturation and prediction of ART outcomes.

[0041] Example 1

[0042] 1. Research objectives:

[0043] Explore the relationship between specific biomarkers and ovarian function-related indicators (number of eggs retrieved and number of embryo cleavages), and verify their application value in predicting ovarian function-related indicators.

[0044] 2. Sample collection-time and location:

[0045] 2.1. Samples were collected at the Obstetrics and Gynecology Hospital, Zhejiang University School of Medicine from October 2014 to April 2015.

[0046] 2.2. Sample source: 30 women with tubal infertility who received assisted reproductive technology were selected and divided into an elderly group and a control group, with 15 cases in each group.

[0047] 3. Grouping criteria:

[0048] 3.1. Older age group: The female age was between 39 and 47 years old. The inclusion criteria were that the female age was ≥ 40 years old and had tubal infertility.

[0049] 3.2. Control group: The females were between 27 and 34 years old. The inclusion criteria were that the females were <30 years old and had tubal infertility.

[0050] 4. Exclusion criteria:

[0051] 4.1. Older age group: The female is less than 40 years old. Suffering from underlying diseases such as diabetes, hypertension, thyroid dysfunction, etc. Suffering from polycystic ovary syndrome, endometriosis. Preimplantation genetic testing (PGT) is required due to chromosomal abnormalities or genetic diseases. Previous ovarian surgery history.

[0052] 4.2. Control group: Females aged ≥ 30 years. Suffering from underlying diseases such as diabetes, hypertension, thyroid dysfunction, etc. Suffering from polycystic ovary syndrome, endometriosis. Preimplantation genetic testing (PGT) is required due to chromosomal abnormalities or genetic diseases. Previous ovarian surgery history.

[0053] 5. Obtaining follicular fluid samples:

[0054] 5.1. Egg retrieval through vaginal ovarian puncture: Under the guidance of transvaginal B-ultrasound, the subjects underwent transvaginal ovarian puncture and egg retrieval to aspirate follicular fluid.

[0055] 5.2. Centrifugation: Transfer the aspirated follicular fluid to a low-temperature centrifuge, set the temperature to 4°C, speed to 2000 rpm, and centrifuge for 10 minutes.

[0056] 5.3. Aliquot and store: After centrifugation, aspirate the supernatant and dispense it into sterile sample tubes, 1 ml per tube, and then place in a -80℃ refrigerator for later use.

[0057] 6. Analysis of follicular fluid metabolites:

[0058] 6.1. Analytical technology: Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) techniques were used to comprehensively analyze the metabolic components of follicular fluid.

[0059] 6.2. Sample information management: The detailed information of each follicular fluid sample is entered into the Laboratory Information Management System (LIMS). The system will assign a unique identifier to each sample, which is only associated with the original identifier to ensure the accuracy and traceability of the sample information.

[0060] 6.3. Sample preparation process: using Hamilton's automatic The sample was prepared by the system. The sample was placed in methanol and shaken vigorously for 2 minutes to induce protein precipitation, thereby dissociating small molecules bound to the protein and recovering metabolites of different chemical structures. Centrifugation was performed after the shaking was completed. The supernatant obtained by centrifugation was divided into 5 equal parts and the subsequent test sample preparation was carried out according to a specific process. One part was used for UPLC-MS / MS analysis in positive ion mode electrospray ionization, which aims to obtain relevant information about metabolites from a specific ion mode. One part was used for UPLC-MS / MS analysis in negative ion mode electrospray ionization, which complements the positive ion mode analysis to fully explore the characteristics of metabolites. One part was used for LC polarity platform analysis to further analyze the metabolite composition from the perspective of polarity. One part was used for GC-MS analysis, which used the separation ability of gas chromatography and the identification ability of mass spectrometry to deeply analyze the metabolite composition. One sample was retained as a backup in case of unexpected situations during the experiment or the need for additional verification experiments.

[0061] 6.4. Sample pretreatment details: Place the sample (Zymark) equipment to effectively remove organic solvents. For samples used for LC analysis, they need to be stored overnight in a nitrogen environment to allow the sample state to meet the analysis requirements before analysis preparation.

[0062] 6.5. Specific analysis instruments and parameters:

[0063] 6.5.1. Liquid chromatography-tandem mass spectrometry (LC-MS / MS): Platform construction: Based on Waters Aquity ultra-high performance liquid chromatograph (UPLC) and Thermo-Finnigan LTQ mass spectrometer. It runs at nominal mass resolution and is equipped with an electrospray ionization source (ESI) and a linear ion trap (LIT) mass spectrometer to ensure accurate analysis of metabolites in samples.

[0064] 6.5.2 Sample Dissolution: The sample extract is first dried and then redissolved in an acidic or alkaline LC-compatible solvent. Twelve or more injection standards with fixed concentrations are added to each solvent as a reference during the analysis.

[0065] 6.5.3. Analysis conditions: One sample was analyzed under acidic positive ion optimization conditions, and the other under alkaline negative ion optimization conditions, using two independent injections using two independent dedicated columns (Waters UPLCBEHC18-2.1×100mm, 1.7μm). Under acidic conditions, the re-dissolved extract was gradient eluted with water and methanol containing 0.1% formic acid; the alkaline extract also used water / methanol, but it contained 6.5mM ammonium bicarbonate. Mass spectrometry analysis was performed alternately between MS and data-dependent MS / MS scans, using dynamic exclusion technology, and the scan range was set to 80-1000m / z.

[0066] 6.5.4 Gas Chromatography-Mass Spectrometry (GC-MS): Sample pretreatment: Samples for GC-MS analysis are derivatized with bis(trimethylsilyl)trifluoroacetamide under dry nitrogen. Before derivatization, the samples must be dried in a vacuum oven for at least 18 hours to ensure that the sample state meets the derivatization requirements.

[0067] 6.5.5. Separation and Detection: The derivatized samples were separated on a 5% diphenyl / 95% dimethylpolysiloxane fused silica column (20 m x 0.18 mm inner diameter; 0.18 μm film thickness) with helium as the carrier gas and the temperature was linearly increased from 60°C to 340°C in 17.5 min.

[0068] The samples were analyzed on a Thermo-Finnigan Trace DSQ fast scanning single quadrupole mass spectrometer, using electron impact ionization (EI) mode and running at unit mass resolution power, with a scan range of 50 to 750 m / z. Raw data files were archived and extracted according to established methods.

[0069] 6.5.6. Technical Replica Application: A small mixture of each sample (colored cylinder) is made into CMTRX technical replica samples (multi-colored cylinders). During the entire platform operation, these technical replica samples are injected one by one in sequence. The variability between the consistently detected biochemical indicators is used to estimate the variability of the entire process and platform, thereby evaluating the stability and reliability of the experiment.

[0070] 6.5.7. Blank control: Extract pure water samples as blank control samples. Since the water samples themselves do not contain various endogenous compounds in the follicular fluid, during the test process, all experimental operations are performed on the blank control water samples and the actual follicular fluid samples simultaneously. Once a signal related to the target metabolite appears in the test results of the blank control water sample, it is very likely that there is contamination in the test system. At this time, the test work must be suspended immediately, and the entire experimental system must be fully inspected and cleaned. After eliminating possible sources of contamination, retest to ensure that the subsequent test results are true and reliable.

[0071] 6.5.8 Internal standard mixture: A group of internal standard mixtures is carefully selected, and each substance in it is strictly screened to ensure that it will not interfere with the measurement of endogenous compounds in the follicular fluid sample during the entire detection process. In the early stage of sample processing, the internal standard mixture is added to the follicular fluid sample in a precise proportion, so that it undergoes the entire experimental process together with the metabolites in the sample, including sample pretreatment, separation, detection and other links. Since the content of each substance in the internal standard mixture is known and accurately determined, after the test is completed, by comparing the known addition amount of these internal standards with the actual detected amount, the entire experimental process can be effectively monitored and corrected. For example, if it is found that the actual detection amount of a certain internal standard is deviated from the known addition amount, it can be analyzed whether there is loss of the sample during the processing process (such as adsorption on the container wall, volatilization, etc.), or whether the response of the detection instrument fluctuates (such as sensitivity changes, etc.), and then the detection results of endogenous compounds in the follicular fluid sample are corrected accordingly to improve the reliability of the test data.

[0072] 6.5.8. Method for determining instrument variability - core indicator: The instrument variability is determined by calculating the relative standard deviation (RSD) of the median value of the internal standard added to each test sample before injection into the mass spectrometer.

[0073] Internal standard function: The internal standard is a known substance added to the test sample, which has the characteristics of stable chemical properties, good coexistence with other components in the sample and no reaction. During the entire test process, the internal standard and the target analyte in the sample will undergo the same processing steps and test conditions, so it can be used as a "reference" to help evaluate the accuracy of the entire test process and the performance of the instrument.

[0074] Relative standard deviation (RSD) principle: RSD is an important indicator used in statistics to measure the degree of dispersion of a set of data. In this experimental scenario, after measuring the internal standard added to each test sample, a series of data will be obtained, and RSD will be calculated based on these data.

[0075] The calculation formula is: RSD = standard deviation / average value × 100%. The standard deviation reflects the degree of deviation of each measured value from the average value, and RSD presents this deviation in the form of a relative value, which is convenient for intuitively comparing the discrete degree of different groups of data.

[0076] In this example, instrument variability was determined by calculating the median relative standard deviation (RSD) of the internal standard added prior to each sample injection, and overall process variability was determined by calculating the median RSD of all endogenous metabolites (i.e., non-instrument standards) in 100% matrix samples, which were technical replicates of the pooled samples.

[0077] The specific data are as follows Table 1:

[0078] Table 1

[0079] QC Samples Measurement Median RSD Internal Standards Instrument variability 5％ Endogenous biochemicals Total process variability 10％

[0080] 6.5.9. Result statistics and analysis:

[0081] Data representation and comparison: Continuous variables were expressed as mean ± standard deviation (SD). For the inter-group differences of continuous variables, t-test was used for comparative analysis to determine whether there were significant differences between the two groups of data.

[0082] Data dimensionality reduction: Use principal component analysis (PCA) to reduce the dimensionality of data, reduce the complexity of the data, and extract the main features for better data analysis.

[0083] Comparison of metabolite levels: The t-test was used to compare the levels of 311 metabolites between the elderly group and the control group in detail to clarify the differences in metabolite levels between the two groups.

[0084] Correlation analysis: With the help of Pearson correlation analysis, we deeply explored the correlation between each differential metabolite and age, number of eggs obtained and number of embryo cleavage, revealing the potential connection between metabolites and these key indicators. Visual presentation of results: Use heat maps to intuitively distinguish the differential metabolites between the two groups, and display the standardized levels of metabolites in different colors, so that the differences are clear at a glance, which is convenient for intuitive analysis and interpretation of data.

[0085] Figure 1A flow chart of the mass spectrometry sample preparation technique provided for Example 1 of the present application; a small portion of each study sample (colored cylinder) is pooled to create a replicate sample (multi-colored cylinder), which is then injected periodically throughout the process; for studies spanning multiple days, a data normalization step is performed to correct for variations caused by differences in instrument tuning between days; the variability between consistently tested biochemistries is used to calculate an estimate of the variability of the entire process.

[0086] Figure 1 The English interpretation is as follows:

[0087] Studysamples is interpreted in Chinese as: research samples.

[0088] DAY1 is interpreted as: first day in Chinese.

[0089] DAY2 is translated into Chinese as: the second day.

[0090] CMTRX: Technical replicates created from an equal number of all client study samples.

[0091] Processblank is interpreted as: process blank in Chinese.

[0092] Study samples randomized and balanced is interpreted in Chinese as: study samples randomized and balanced.

[0093] 1stinjection is explained in Chinese as: the first injection.

[0094] Finalinjection is interpreted in Chinese as: final injection.

[0095] Figure 2 The principal component analysis (PCA) diagram, heat map and metabolite levels of different groups provided in Example 1 of the present application; (A), PCA diagram and 2D cluster diagram describing the separation trend of the two groups, red dots and green dots represent elderly and young women, respectively; (B), the levels of each metabolite in the elderly group and the young control group were tested by unpaired Student's t-test, and then the p-value of the t-test was adjusted by the FDR method to obtain the q-value considering multiple comparisons, and the Foldchange was calculated as the ratio of the mean metabolite of the elderly group to that of the young control group; (C), heat map of hierarchical cluster analysis, the standardized levels of metabolites are represented by different colors;

[0096] Figure 2 The English interpretation of Part (A) is as follows:

[0097] PC1 (18.2%) is explained in Chinese as: principal component 1 (accounting for 18.2%), which means the first principal component and its proportion in principal component analysis.

[0098] PC2 (8.7%) is explained in Chinese as: principal component 2 (accounting for 8.7%), which refers to the second principal component in principal component analysis and its proportion.

[0099] Control is explained in Chinese as: control group, the group used for comparison in the experiment.

[0100] Aging is explained in Chinese as: aging group, a group used to study aging-related conditions in experiments.

[0101] Figure 2 The English interpretation of Part (B) is as follows:

[0102] Foldchange (Aging / Control) is explained in Chinese as: fold change (aging group / control group), which is used to compare the fold difference between the data of the aging group and the control group.

[0103] -log10(p-value) is interpreted in Chinese as: negative log 10 (p value). In statistical analysis, it is used to indicate the value of the p value after negative log 10 conversion.

[0104] Participants is interpreted in Chinese as: participants, referring to the people who participate in the experiment.

[0105] Figure 2 The English interpretation of part (C) is as follows:

[0106] Metabolites is explained in Chinese as: metabolites, which refer to substances involved in metabolism in organisms.

[0107] Creatine is explained in Chinese as: Creatine, a substance involved in energy metabolism in organisms.

[0108] meValonate is explained in Chinese as: mevalonate, an important intermediate metabolite in biosynthesis.

[0109] Gamma-glutamylvaline is explained in Chinese as: γ-glutamylvaline, an amino acid.

[0110] Choline is explained in Chinese as: choline, a nutrient that is important for the nervous system and cell membrane structure.

[0111] Histidine is explained in Chinese as: histidine, an amino acid.

[0112] Methionine is explained in Chinese as methionine, a sulfur-containing amino acid.

[0113] trans-4-hydroxypyroline is explained in Chinese as: trans-4-hydroxyproline, an amino acid.

[0114] N2,N2-dimethylguanosine is explained in Chinese as: N2,N2-dimethylguanosine, a nucleoside.

[0115] Figure 3 The differential metabolites in the follicular fluid of the advanced-age group and the control group provided in Example 1 of the present application, and their correlation with maternal age, number of retrieved oocytes and number of cleavage embryos; (A), comparison of selected metabolites in the follicular fluid of the advanced-age group and the young control group (t-test); (B), correlation between metabolites and age (Pearson correlation test), (C), correlation between metabolites and the number of retrieved oocytes (Pearson correlation test); (D), correlation between metabolites and the number of cleavage embryos (Pearson correlation test).

[0116] Figure 3 The English interpretation is as follows:

[0117] Control is the Chinese translation of "control group". In an experiment, it is a group used as a comparison standard.

[0118] Aging in Chinese means: aging group. In experiments, it is a group used to study aging-related phenomena.

[0119] Group is explained in Chinese as: group. In an experiment, the unit for classifying experimental objects.

[0120] Age (years) is interpreted as: age (in years). It is used to indicate the age of the experimental subjects.

[0121] Numberofoocytes means the number of oocytes in the experimental subject.

[0122] Cleavedembryos means "splitting embryo" in Chinese. It refers to an embryo that has begun to split.

[0123] Anova is explained in Chinese as: analysis of variance. A statistical analysis method used to compare whether there are significant differences in the means between multiple groups.

[0124] p is explained in Chinese as: p value. In statistical hypothesis testing, it is an indicator used to determine whether the results are statistically significant.

[0125] R in Chinese means: correlation coefficient. It is a statistical indicator used to measure the strength of the linear relationship between two variables.

[0126] Figure 4 The English interpretation is as follows:

[0127] Youngage in Chinese means: young age. It refers to the state of an organism when it is younger.

[0128] Advancedage is defined in Chinese as the old age. It refers to the state of an organism when it is older.

[0129] Glucose is a monosaccharide that is an important energy source in living organisms.

[0130] Fat is explained in Chinese as: fat. It is a substance that stores energy in living organisms.

[0131] Protein is explained in Chinese as: protein. It is composed of amino acids and is an important component of organisms.

[0132] Betaine is explained in Chinese as betaine, a substance that has multiple functions in organisms.

[0133] Choline is explained in Chinese as choline. It is a nutrient that is important for the nervous system and cell membrane structure.

[0134] Cysteine ​​is explained in Chinese as: cysteine, an amino acid.

[0135] Methioninemetabolism is explained in Chinese as: methionine metabolism. The metabolic process of methionine in organisms.

[0136] SAM is explained in Chinese as: S-adenosylmethionine, which is an important metabolic intermediate.

[0137] Hcy is interpreted in Chinese as homocysteine, a sulfur-containing amino acid.

[0138] Elevatedoxidativestress is explained in Chinese as: elevated oxidative stress. The balance between oxidation and anti-oxidation in the body is broken, tending to the state of oxidation.

[0139] Mitochondrialdysregulation is explained in Chinese as: mitochondrial disorder. The state in which mitochondrial function is abnormal.

[0140] Glycolysis is the process of glucose breaking down into pyruvic acid.

[0141] Fattyacids is explained in Chinese as: fatty acids. The components of fat.

[0142] β-Oxidation is the process of oxidative decomposition of fatty acids in organisms.

[0143] Pyruvate is explained in Chinese as pyruvic acid, the final product of glycolysis.

[0144] Acetyl-CoA is explained in Chinese as: acetyl coenzyme A. It is an important metabolic intermediate in organisms.

[0145] Cholesterol is a lipid that is important for the structure and function of cell membranes.

[0146] Aminoacids is explained in Chinese as: amino acids. The basic building blocks of proteins.

[0147] Malate is explained in Chinese as malic acid, an organic acid in living organisms.

[0148] TCAcycle is explained in Chinese as: tricarboxylic acid cycle, also known as citric acid cycle, is an important metabolic pathway in organisms.

[0149] α-Ketoglutarate is an intermediate product in the tricarboxylic acid cycle.

[0150] Succinate is an intermediate product in the tricarboxylic acid cycle.

[0151] Ketonebodies are the products synthesized from fatty acids in the liver.

[0152] Valine is explained in Chinese as valine, an amino acid.

[0153] GGT in Chinese means: γ-glutamyl transpeptidase, an enzyme.

[0154] Gamma-glutamylvaline is an amino acid.

[0155] GSH is explained in Chinese as glutathione. It is an antioxidant.

[0156] GSSG in Chinese means: Oxidized glutathione. It is the oxidized form of glutathione.

[0157] Follicular fluid is the fluid present in the ovarian follicles.

[0158] Creatine is explained in Chinese as: Creatine, a substance that participates in energy metabolism in organisms.

[0159] Phosphocreatine is explained in Chinese as creatine phosphate. The phosphorylated form of creatine is an energy storage substance.

[0160] Lactate is explained in Chinese as lactic acid, one of the final products of glycolysis.

[0161] Histidine is explained in Chinese as: histidine, an amino acid.

[0162] Histamine is explained in Chinese as: histamine. It is formed by decarboxylation of histidine and has a variety of physiological effects.

[0163] Glutamine is explained in Chinese as glutamine, an amino acid.

[0164] Acetoacetate is explained in Chinese as acetoacetic acid, a type of ketone body.

[0165] Anaerobicmetabolites are explained in Chinese as: anaerobic metabolites. Metabolites produced under anaerobic conditions.

[0166] Oxidation energy is the energy released by oxidation reaction.

[0167] Experimental results: A total of 311 small molecule metabolites were detected in this example. Statistical analysis showed that 70 of them were significantly different between the elderly group and the control group (p<0.05). The contents of creatine, histidine, methionine, trans-4-hydroxyproline, choline, mevalonate and γ-glutamylvaline in the follicular fluid of the elderly group were significantly higher than those in the control group. Correlation analysis showed that 8 of the metabolites were significantly positively correlated with age, 3 metabolites were negatively correlated with the number of eggs retrieved, and 5 metabolites were negatively correlated with the number of embryonic cleavages.

[0168] In summary, the metabolomics of follicular fluid of older women showed an expression profile that was significantly different from that of young women. Eight small molecule metabolites in follicular fluid were significantly correlated with age, three small molecule metabolites were significantly correlated with the number of oocytes retrieved, and five small molecule metabolites were significantly correlated with the number of embryonic cleavages. These differential metabolites focus on lipid metabolism, steroid metabolism, and redox homeostasis, and may be expected to become biomarkers for predicting ovarian aging and oocyte developmental potential.

[0169] Experimental results: A total of 311 small molecule metabolites were detected in Example 1. Statistical analysis showed that 70 metabolites were significantly different between the elderly group and the control group (p<0.05). Among them, the contents of creatine, histidine, methionine, trans-4-hydroxyproline, choline, mevalonate and γ-glutamylvaline in the follicular fluid of the elderly group were significantly higher than those of the control group.

[0170] Correlation analysis showed that 8 metabolites were significantly positively correlated with age, 3 metabolites were negatively correlated with the number of oocytes retrieved, and 5 metabolites were negatively correlated with the number of embryo cleavages.

[0171] Summary of the technical effects of this application: This application further clarifies the relationship between a specific biomarker combination and ovarian function-related indicators (number of eggs obtained and number of embryonic cleavages) through a systematic, comprehensive and rigorous experimental design in Example 1. The first biomarker (including at least one of histidine, methionine and trans-4-hydroxyproline) was determined to be used to predict the number of eggs obtained, and the second biomarker (including at least one of histidine, trans-4-hydroxyproline, choline, N2, N2-dimethylguanosine and γ-glutamylvaline) was used to predict the number of embryonic cleavages. Compared with the current clinically commonly used evaluation methods that only rely on age, basal antral follicle count (AFC), anti-Mullerian hormone (AMH) and morphological methods, the biomarker combination provided in this application can more objectively and accurately predict ovarian function-related indicators, effectively overcoming the limitations of existing evaluation methods in quantifying complex physiological changes in the ovaries and the developmental potential of oocytes.

[0172] Secondly, this application is based on the accurate prediction of the number of eggs obtained and the number of embryo cleavages. Doctors can understand the patient's ovarian function status and oocyte development potential in advance and accurately before the implementation of assisted reproductive technology (ART). Based on these accurate prediction results, doctors can develop more targeted and personalized treatment strategies for patients. For example, more scientific and reasonable decisions can be made in terms of dosage adjustment of ovulation-inducing drugs, schedule of use, and selection of embryo transfer timing, thereby greatly improving the success rate of assisted reproductive technology, bringing more fertility hope to infertile patients, and improving their fertility outcomes.

[0173] Thirdly, this application adopted strict sample collection standards and processing procedures during the experiment, applied advanced and mature analytical techniques, such as liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), etc., and implemented comprehensive and meticulous quality control measures. These measures ensure the reliability and accuracy of the experimental results, provide solid technical support for the application of biomarkers in predicting ovarian function-related indicators, and strongly promote the further development of reproductive medicine in ovarian function assessment and infertility treatment, which has important clinical application value and scientific research significance.

[0174] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. The use of biomarkers in the preparation of products for predicting ovarian function-related indicators, characterized in that: The ovarian function related indicators include the number of eggs obtained and the number of embryo cleavages; The biomarkers include a first biomarker and a second biomarker; The first biomarker is used to predict the number of oocytes retrieved; The second biomarker is used to predict the embryo cleavage number; The first biomarker includes at least one of histidine, methionine, and trans-4-hydroxyproline; The second biomarker includes at least one of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine and γ-glutamylvaline.

2. The use according to claim 1, characterized in that: The first biomarker includes at least two of histidine, methionine, and trans-4-hydroxyproline.

3. The use according to claim 1, characterized in that: The second biomarker comprises at least two of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine and γ-glutamylvaline.

4. The use according to claim 1, characterized in that: The first biomarker includes histidine, methionine and trans-4-hydroxyproline.

5. The use according to claim 1, characterized in that: The second biomarker includes at least four of histidine, trans-4-hydroxyproline, choline, N2,N2-dimethylguanosine and γ-glutamylvaline.

6. The use according to any one of claims 1 to 5, characterized in that: The product also includes an extraction reagent for extracting the biomarkers from a biological sample; the biological sample is derived from the follicular fluid of a subject.

7. The use according to claim 6, characterized in that: The product also includes a tool assembly for collecting, storing and pre-processing follicular fluid samples.

8. The use according to claim 6, characterized in that: The content of the first biomarker is negatively correlated with the number of eggs obtained in the biological sample; The content of the second biomarker is negatively correlated with the embryo cleavage number in the biological sample.

9. The use according to claim 1, characterized in that: The content of the biomarker is detected by liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry detection technology.

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