WO2022006941A1 - 预测受试者出现卵巢储备新变化年限的系统和方法 - Google Patents

预测受试者出现卵巢储备新变化年限的系统和方法 Download PDF

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WO2022006941A1
WO2022006941A1 PCT/CN2020/102088 CN2020102088W WO2022006941A1 WO 2022006941 A1 WO2022006941 A1 WO 2022006941A1 CN 2020102088 W CN2020102088 W CN 2020102088W WO 2022006941 A1 WO2022006941 A1 WO 2022006941A1
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ovarian reserve
ovarian
age
level
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PCT/CN2020/102088
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English (en)
French (fr)
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徐慧玉
李蓉
乔杰
冯国双
韩勇
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北京大学第三医院（北京大学第三临床医学院）
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B10/00—Other methods or instruments for diagnosis, e.g. instruments for taking a cell sample, for biopsy, for vaccination diagnosis; Sex determination; Ovulation-period determination; Throat striking implements
    • A61B10/0012—Ovulation-period determination
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/74—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving hormones or other non-cytokine intercellular protein regulatory factors such as growth factors, including receptors to hormones and growth factors

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  • the present invention relates to an optimized system for evaluating a subject's ovarian reserve, by which the subject's own ovarian reserve can be assessed, and a subject can be assessed according to the subject's current ovarian reserve
  • the number of years required to achieve a certain ovarian reserve status such as near-depletion of ovarian reserve, early decline in fertility due to decreased ovarian reserve, and significant decline in fertility due to decreased ovarian reserve age).
  • Ovarian reserve refers to the number of primordial follicles contained in the ovarian cortex. It reflects the ability of the ovaries to provide healthy, fertile eggs and is the most important indicator of female fertility. Generally speaking, the higher the number of primordial follicles, the better the quality and the higher the probability of conception. Ovarian reserve decreases with age, and the better the ovarian reserve, the higher its fertility. The number of primordial follicles reaches about 6-7 million in the second trimester, after which some atresia occurs, and there are about 1-2 million primordial follicles at birth.
  • the number of primordial follicles at the onset of puberty is around 300,000-500,000, and at the age of menopause, the number is around 1000.
  • the population of primordial follicles is highly heterogeneous, ranging from tens of thousands to millions at birth, which is the main reason for the large variation in age at natural menopause (ANM) in women. Fertility decline begins around 10 years before menopause, so the age at which fertility decline begins varies widely.
  • assisted reproductive therapy has a very limited role in DOR patients or perimenopausal women, when their fertility has been significantly reduced or is close to depletion, and the follicles in the ovary are very limited. There are few or no numbers, and there is an international consensus that even with expensive ovulation induction treatments, the fertility of such people cannot be improved.
  • a system for evaluating the ovarian reserve function of a subject which includes: a data acquisition module, which is used to obtain the age of the subject, anti-Mullerian hormone (AMH) ) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC) data; and a module for calculating ovarian reserve, which is used to calculate the above-mentioned information obtained in the data acquisition module, thereby calculating the subject The probability of low ovarian response (p).
  • AMH anti-Mullerian hormone
  • FSH follicle-stimulating hormone
  • AFC tral follicle count
  • the receiver operating characteristic (ROC) curve is used to detect the cut-off points of age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, antral follicle count (AFC), and according to the Cut-point values for cut-off points to convert age, anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC) into dichotomous variables to use as predictors variable to calculate the subject's probability of low ovarian response (p).
  • AMD anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • AFC tral follicle count
  • the above-mentioned system can effectively calculate the probability of the subject's low ovarian response, and further, the default ovarian reserve function grouping parameters are pre-stored in the grouping module included in the system.
  • the low response probability p is grouped, so that the ovarian reserve level of the subjects can be grouped.
  • the probability of low ovarian response of the subject can be calculated, and further, the ovarian reserve level of the subject can be grouped according to the probability of the low ovarian response.
  • the system can be used to calculate the parameter (p) for predicting the low response probability of the ovary of the subject, and group the ovarian reserve function of the subject according to the default ovarian reserve function grouping parameters pre-stored in the system, To determine the level of ovarian reserve function, and evaluate the level of ovarian reserve.
  • a system for assessing ovarian reserve in a subject includes: a data acquisition module for acquiring the age of the subject, anti-Mullerian hormone (AMH) level , follicle-stimulating hormone (FSH) level data; and a module for calculating ovarian reserve, which is used to calculate the above-mentioned information obtained in the data acquisition module, thereby calculating the probability (p) of the subject's ovarian hyporesponsiveness .
  • AMH anti-Mullerian hormone
  • FSH follicle-stimulating hormone
  • the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level are converted into multi-category variables, and the subjects are calculated using the multi-category variables as predictors Probability of low ovarian response (p).
  • the current probability of low ovarian response of the subject can be calculated, and the ovarian reserve level of the subject can be further grouped according to the probability of low ovarian response.
  • a parameter (p 0 ) for predicting the probability of low ovarian response in the subject can be calculated.
  • this application involves the following:
  • a system for predicting the number of years or age at which a new change in ovarian reserve occurs in a subject comprising:
  • a data acquisition module which is used to acquire data on the subject's age, anti-Mullerian hormone (AMH) level, and follicle-stimulating hormone (FSH) level;
  • a module for calculating ovarian reserve which uses multi-category variables that convert the data of the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's follicle-stimulating hormone (FSH) level to calculate the subject
  • the anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in venous blood on any day of the menstrual cycle of a female subject
  • the The follicle stimulating hormone (FSH) level refers to the concentration of follicle stimulating hormone in the venous blood of female subjects on day 2 of menstruation.
  • a subject's ovarian reserve decline to a certain extent refers to the following three conditions:
  • the subject's ovarian reserve has decreased to the point where fertility begins to decline, i.e. the probability of low ovarian response (p) increases to 25%;
  • the subject's ovarian reserve was significantly reduced, resulting in a significant reduction in fertility, that is, the probability of low ovarian response (p) increased to 50%;
  • Predicting the decline of the subject's ovarian reserve to the number of years when fertility begins to decline refers to calculating the subject's current ovarian reserve, that is, the current probability of low ovarian response (p 0 ), using the module for calculating ovarian reserve, and then calculating the The target ovarian reserve, i.e. the number of years required for the probability of low ovarian response (p) to equal 25%;
  • the number of years in which a subject's ovarian reserve is predicted to lead to a significant decline in fertility refers to: calculating the subject's current ovarian reserve, that is, the current probability of low ovarian response (p 0 ), by using the module for calculating ovarian reserve, and then calculating the Target ovarian reserve, i.e. the number of years required for the probability of low ovarian response (p) to equal 50%;
  • Predicting the number of years in which the subject's ovarian reserve is close to depletion leading to depletion of fertility refers to: calculating the subject's current ovarian reserve, that is, the current probability of low ovarian response (p 0 ), using a module that calculates ovarian reserve, and then calculating the goal of reaching the target Ovarian reserve, i.e. the number of years required for the probability of low ovarian response (p) to be equal to 95%.
  • the subject age is converted into a three-category variable
  • the age of the subjects is divided into three groups, namely: the age of the subjects is 30 years old and below, the age of the subjects is more than 30 years old and 40 years old and below, and the age of the subjects is more than 40 years old .
  • the subject's follicle stimulating hormone (FSH) level was converted into a four-category variable
  • the subjects' FSH levels were divided into four groups, namely: subjects whose FSH levels were less than 6.5IU/L, and subjects whose FSH levels were 6.5 IU/L. and IU/L or more and less than 8.5IU/L, the subject's vesicle-stimulating hormone (FSH) level is 8.5IU/L and above and less than 10.5IU/L, and the subject's vesicle-stimulating hormone (FSH) level is 10.5IU/L and above.
  • the subject's anti-Mullerian hormone (AMH) level was converted into a five-category variable
  • the subjects' anti-Mullerian hormone (AMH) levels were divided into five groups, namely: subjects whose anti-Mullerian hormone (AMH) levels were less than 0.5 ng/ml, subjects whose anti-Mullerian hormone (AMH) levels were less than 0.5 ng/ml
  • the subject's anti-Mullerian hormone (AMH) level is 1 ng/ml and above and less than 1.5ng/ml, and the subject's
  • the anti-Mullerian hormone (AMH) level of the subject is 1.5 ng/ml and above and less than 2 ng/ml, and the subject's anti-Mullerian hormone (AMH) level is greater than or equal to 2 ng/ml.
  • the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's follicle stimulating hormone (FSH) based on the existing database are stored in advance
  • a formula for predicting a subject's current probability of low ovarian response (p 0 ) was fitted to the multi-category variables transformed from the horizontal data.
  • the formula is the following formula one:
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • a is any value selected from -4.072 ⁇ -3.188, a is preferably -3.630;
  • age is 0 when the subject is 30 years old and younger
  • age is 1
  • b is any value selected from 0.163 to 0.960, b is preferably 0.561, and
  • age When the age of the subject is greater than 40 years old, age is 1, b is any value selected from 0.295 to 1.317, and b is preferably 0.806;
  • FSH vesicle stimulating hormone
  • FSH vesicle stimulating hormone
  • FSH vesicle stimulating hormone
  • FSH vesicle stimulating hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • the following formula 3 is used to calculate the subject from the current ovarian reserve (p 0 ) to the significant decline in ovarian reserve that leads to a significant decline in fertility, that is, the ovarian reserve is significantly reduced. Years with a 50% probability of low response:
  • age2 represents the age at which the ovarian reserve of the subject is significantly decreased
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the following formula 4 is used to calculate the subject's current ovarian reserve (p 0 ) to the near-depletion of ovarian reserve (fertility is close to depletion), That is, the number of years in which the probability of low ovarian response is 95%:
  • age3 represents the age at which the ovarian reserve of the subject is close to depletion
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the following formula 5 is used to calculate the subject from the current ovarian reserve (p 0 ) to the decline in ovarian reserve that leads to a decline in fertility, that is, low ovarian response Years with 25% probability:
  • age4 represents the age at which the ovarian reserve of the subject begins to decline
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • a method for predicting the number of years or age at which a new change in ovarian reserve occurs in a subject comprising:
  • a data collection step which obtains data on the subject's age, anti-Mullerian hormone (AMH) level, and follicle-stimulating hormone (FSH) level;
  • Ovarian reserve was calculated using a multi-categorical variable that transformed the data of the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's follicle-stimulating hormone (FSH) level into The current probability of low ovarian response (p 0 ), that is, the current ovarian reserve;
  • AMH anti-Mullerian hormone
  • FSH follicle-stimulating hormone
  • the step of calculating the number of years in which the subject's ovarian reserve has declined to a certain degree is calculated by using the subject's current ovarian reserve, that is, the probability of low ovarian response (p 0 ), to calculate the number of years or age at which the subject has a new change in ovarian reserve.
  • the anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in the venous blood of a female subject on any day of the menstrual cycle
  • the follicles Stimulating hormone (FSH) level refers to the concentration of follicle stimulating hormone in the venous blood of female subjects on day 2 of menstruation.
  • a subject's ovarian reserve decline to a certain extent refers to the following three conditions:
  • the subject's ovarian reserve has decreased to the point where fertility begins to decline, i.e. the probability of low ovarian response (p) increases to 25%;
  • the subject's ovarian reserve was significantly reduced, resulting in a significant reduction in fertility, that is, the probability of low ovarian response (p) increased to 50%;
  • the near-depletion of the subject's ovarian reserve resulted in near-depletion of fertility, ie the probability (p) of low ovarian response increased to 99%.
  • Predicting the decline of the subject's ovarian reserve to the number of years when fertility begins to decline refers to calculating the subject's current ovarian reserve, that is, the current probability of low ovarian response (p 0 ), using the steps for calculating ovarian reserve, and then calculating the The target ovarian reserve, i.e. the number of years required for the probability of low ovarian response (p) to equal 25%;
  • the number of years in which a subject's ovarian reserve is predicted to lead to a significant decline in fertility refers to: calculating the subject's current ovarian reserve, that is, the current probability of low ovarian response (p 0 ), using the steps for calculating ovarian reserve, and then calculating the Target ovarian reserve, i.e. the number of years required for the probability of low ovarian response (p) to equal 50%;
  • Predicting the number of years in which the subject's ovarian reserve is close to depletion leading to depletion of fertility refers to: calculating the subject's current ovarian reserve, i.e., the current probability of low ovarian response (p 0 ), using the steps for calculating ovarian reserve, and then calculating the achievement of the target Ovarian reserve, i.e. the number of years required for the probability of low ovarian response (p) to be equal to 95%.
  • the subject age was converted into a three-category variable
  • the age of the subjects is divided into three groups, namely: the age of the subjects is 30 years old and below, the age of the subjects is more than 30 years old and 40 years old and below, and the age of the subjects is more than 40 years old .
  • the subject's follicle stimulating hormone (FSH) level was converted into a four-category variable
  • the subjects' FSH levels were divided into four groups, namely: subjects whose FSH levels were less than 6.5IU/L, and subjects whose FSH levels were 6.5 IU/L. and IU/L or more and less than 8.5IU/L, the subject's vesicle-stimulating hormone (FSH) level is 8.5IU/L and above and less than 10.5IU/L, and the subject's vesicle-stimulating hormone (FSH) level is 10.5IU/L and above.
  • the subject's anti-Mullerian hormone (AMH) level was converted into a five-category variable
  • the subjects' anti-Mullerian hormone (AMH) levels were divided into five groups, namely: subjects whose anti-Mullerian hormone (AMH) levels were less than 0.5 ng/ml, subjects whose anti-Mullerian hormone (AMH) levels were less than 0.5 ng/ml
  • the subject's anti-Mullerian hormone (AMH) level is 1 ng/ml and above and less than 1.5ng/ml, and the subject's
  • the anti-Mullerian hormone (AMH) level of the subject is 1.5 ng/ml and above and less than 2 ng/ml, and the subject's anti-Mullerian hormone (AMH) level is greater than or equal to 2 ng/ml.
  • the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's follicle stimulating hormone (FSH) based on the existing database are stored in advance
  • a formula for predicting a subject's current probability of low ovarian response (p 0 ) was fitted to multi-category variables transformed from the level of data.
  • the formula is the following formula one:
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • a is any value selected from -4.072 ⁇ -3.188, a is preferably -3.630;
  • age is 0 when the subject is 30 years old and younger
  • age is 1
  • b is any value selected from 0.163 to 0.960, b is preferably 0.561, and
  • age When the age of the subject is greater than 40 years old, age is 1, b is any value selected from 0.295 to 1.317, and b is preferably 0.806;
  • FSH vesicle stimulating hormone
  • FSH vesicle stimulating hormone
  • FSH vesicle stimulating hormone
  • FSH vesicle stimulating hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • AMH anti-Mullerian hormone
  • age2 represents the age at which the ovarian reserve of the subject is significantly decreased
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the following formula 4 is used to calculate the subject's current ovarian reserve (p 0 ) to when the ovarian reserve is nearly exhausted (fertility is close to exhaustion), That is, the number of years in which the probability of low ovarian response is 95%:
  • age3 represents the age at which the ovarian reserve of the subject is close to depletion
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the following formula 5 is used to calculate the subject from the current ovarian reserve (p 0 ) to the decline in the ovarian reserve that causes the fertility to begin to decline, that is, the ovarian response is low. Years with 25% probability:
  • age4 represents the age at which the ovarian reserve of the subject begins to decline
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • Such a population with reduced ovarian reserve can be early identified according to the inventor's system. Utilizing the system and method of the present application, it is possible to help subjects to predict the number of years at which key changes in their ovarian reserve occur, and to suggest that the population should try to conceive as early as possible before entering the DOR.
  • the number and quality of follicles undergo profound changes with age, but the process of ovarian aging has not received enough attention. People often don't realize their fertility may be declining until they have irregular menstruation or menopause, but by the time these signs appear, their fertility is already too low to be enhanced by assisted reproductive technology.
  • the proportion of DOR in each age group in the embodiment of FIG. 1 establishes a logistic curve (growth curve) diagram of the relationship between the proportion of DOR and age.
  • ovarian reserve refers to the number of primordial follicles contained in the ovarian cortex, which is called ovarian reserve. It reflects the ability of the ovaries to provide healthy, fertile eggs and is the most important indicator of ovarian function in women. Generally speaking, the higher the number of primordial follicles, the better the quality and the higher the probability of conception.
  • low ovarian response also referred to as decreased ovarian reserve (DOR) refers to the number of oocytes obtained on the day of oocyte retrieval in the reproductive cycle is less than 5 (ie, 0-4).
  • the number of primordial follicles cannot be assessed non-invasively. It can only be assessed by the number of follicles mobilized in each menstrual cycle. Too few follicles mobilized in an IVF-ET cycle (low ovarian response) indicate a decrease in ovarian reserve.
  • the age factor is generally considered to be the most important factor in evaluating ovarian reserve.
  • a study on age and IVF success rate showed that the IVF success rate was about 26% in women under the age of 30, while the IVF success rate was only 26% when the age was 37 years and older. to 9%.
  • the subject is predicted to have a low response, and the subject is diagnosed as DOR.
  • the mechanism by which ovarian reserve decreases with age is as follows. (1) The number of follicles decreases. Primordial follicles appear after the sex differentiation of the embryo. At this time, the number of follicles is the largest. After puberty, the follicles begin to develop and mature. With the completion of ovulation, a large number of follicles that are recruited but not released shrink and disappear to form the corpus luteum.
  • the number of follicles continues to decrease with age: 20-week-old embryos are the largest in humans, about 6 million follicles, reduced to 700,000 to 2 million in the neonatal period, about 40,000 at puberty, and only more than 1,000 at the beginning of menopause, until completely exhausted.
  • Endocrine factors The hypothalamus-pituitary-ovarian axis regulates women's menstrual cycle and ovulation. Abnormal endocrine levels of this axis can lead to infertility.
  • AMH and inhibin B are secreted by small follicles and are a direct reflection of ovarian reserve. As the ovarian reserve decreases with age, the number of follicles that can be recruited decreases, and therefore the concentration of AMH and inhibin B secreted by it decreases. Inhibin B can negatively feedback regulate the secretion of pituitary FSH, and the decrease of inhibin B level leads to the increase of FSH secretion in the luteal phase. The early increase in FSH promotes the growth of new follicles and E2 secretion, which ultimately shortens the menstrual cycle.
  • the menstrual cycle is the embodiment of ovarian reserve and fertility.
  • the shortening of the menstrual cycle caused by older age, and the reduction of the menstrual cycle by 2-3 days is a sensitive indicator of the aging of the reproductive system, indicating that the follicle growth starts early (the level of FSH increases), and the primordial follicle reserve decreases.
  • Continuous variables In statistics, variables can be divided into continuous variables and categorical variables according to whether the value of the variable is continuous or not.
  • a variable that can take any value within a certain interval is called a continuous variable. Its value is continuous, and two adjacent values can be infinitely divided, which can take infinite values.
  • the specifications and dimensions of production parts, body measurements such as height, weight, chest circumference, etc. are continuous variables, and their values can only be obtained by measurement or measurement.
  • discrete variables whose values can only be calculated in natural numbers or integer units. For example, the number of enterprises, the number of employees, the number of equipment, etc. can only be counted by the number of units of measurement. The value of this variable is generally obtained by counting methods.
  • Categorical variables are variables in terms of geographic location, demographics, etc. that are used to group survey respondents into groups. Descriptive variables describe the difference between a certain customer group and other customer groups. Most categorical variables are also descriptive variables. Categorical variables can be divided into two categories: unordered categorical variables and ordered categorical variables. Among them, the unordered categorical variable refers to the difference in degree and order between the classified categories or attributes. It can be further divided into 1 two categories, such as gender (male, female), drug reaction (negative and positive), etc.; 2 multiple categories, such as blood type (O, A, B, AB), occupation (worker, agriculture, business, learning, military), etc. The ordinal categorical variable has a degree of difference between the categories.
  • Such as urine sugar test results are classified according to -, ⁇ , +, ++, +++; curative effect is classified according to cure, markedly effective, improved, ineffective.
  • ordinal categorical variables firstly, they should be grouped in rank order, the number of observation units in each group should be counted, and the frequency table of ordinal variables (each rank) should be compiled. The obtained data are called rank data.
  • the types of variables are not static. According to the needs of research purposes, various types of variables can be converted. For example, the amount of hemoglobin (g/L) is originally a numerical variable. If it is divided into two categories according to normal and low hemoglobin, it can be analyzed by binomial data; if it is divided into severe anemia, moderate anemia, mild anemia, normal, and hemoglobin When the increase is divided into five grades, the data can be analyzed according to the grades. Sometimes categorical data can also be quantified. If the patient's nausea response can be expressed as 0, 1, 2, or 3, it can be analyzed by numerical variable data (quantitative data).
  • the Logistic function or Logistic curve is a sigmoid function, which was named by Pierre-Institut Velule in 1844 or 1845 when he was studying its relationship with population growth.
  • the generalized logistic curve can mimic the S-shaped curve of population growth (P) in some cases.
  • the initial phase is roughly exponential; then the increase slows as it begins to become saturated; finally, the increase stops when maturity is reached.
  • the present application relates to a system for predicting a new change in ovarian reserve in a subject or a system, comprising: a data acquisition module for acquiring the subject's age, anti-Mullerian hormone (AMH) level, Data on follicle-stimulating hormone (FSH) levels; a module for calculating ovarian reserve using the The data is converted into a multi-category variable to calculate the subject’s current probability of low ovarian response (p 0 ), that is, the current ovarian reserve; Current ovarian reserve, the probability of low ovarian response (p 0 ), was used to calculate the number of years or age at which a new change in ovarian reserve occurred.
  • AMH anti-Mullerian hormone
  • FSH follicle-stimulating hormone
  • the present application relates to a method for predicting the year or age of new changes in ovarian reserve in a subject, comprising: a data acquisition step of acquiring the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulation data on hormone (FSH) levels; ovarian reserve was calculated using data converted from subject age, subject anti-Mullerian hormone (AMH) level, subject follicle stimulating hormone (FSH) level Multi-categorical variables to calculate the subject's current probability of low ovarian response (p 0 ), that is, the current ovarian reserve; the step of calculating the number of years in which the subject's ovarian reserve has declined to a certain extent, which uses the subject's current ovarian reserve That is, the probability of low ovarian response (p 0 ) is used to calculate the number of years or age at which the subject has a new change in ovarian reserve.
  • AMH anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • the method or system of the present application can be used to predict that the subject will experience a decrease in ovarian reserve to a certain extent, resulting in a corresponding change in fertility. Time, i.e. years, or the specific age at which a subject experiences a decline in ovarian reserve to a certain extent.
  • the anti-Mullerian hormone (AMH) level refers to the concentration of anti-Mullerian hormone in venous blood on any day of the menstrual cycle of a female subject
  • the The follicle stimulating hormone (FSH) level refers to the concentration of follicle stimulating hormone in the venous blood of female subjects on day 2 of menstruation.
  • Mullerian hormone is a hormone secreted by the granulosa cells of the small ovarian follicles.
  • the baby girl in the fetal period starts to produce AMH from the 9-month stool.
  • the more small follicles in the ovary the more AMH
  • the AMH concentration will also decrease accordingly, and the closer to menopause, the AMH will gradually tend to 0.
  • Follicle-stimulating hormone is a hormone secreted by basophilic cells in the anterior pituitary gland. FSH can promote the proliferation and differentiation of follicular granulosa cells, and promote the growth of the entire ovary. And its role in testicular seminiferous tubules can promote sperm formation. FSH is secreted in a pulsatile fashion in the human body and changes with the menstrual cycle in women.
  • Determination of serum FSH is of great significance for understanding the pituitary endocrine function, indirectly understanding the functional status of the ovary, evaluating the ovarian reserve and ovarian responsiveness, and formulating the dosage of ovulation induction drugs for the diagnosis and treatment of infertility and endocrine diseases.
  • the occurrence of a decline in ovarian reserve in a subject refers to the following three conditions: the decline in the ovarian reserve of the subject leads to the beginning of decline in fertility, that is, the probability (p) of low ovarian response increases to 25%; subjects with significantly decreased ovarian reserve resulting in significantly decreased fertility, i.e., the probability of low ovarian response (p) increased to 50%; subjects with near-depleted ovarian reserve resulting in near-depleted fertility, i.e., low ovarian response The probability (p) is raised to 95%.
  • ovarian reserve which is converted into a multi-categorical variable using subject age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level
  • AH anti-Mullerian hormone
  • FSH follicle stimulating hormone
  • the age of the subjects is converted into a three-category variable, that is, the age of the subjects is divided into three groups, namely: the age of the subjects is 30 years old and below, the age of the subjects is more than 30 years old and the age of the subjects is 40 years old and below. below, and the subject's age is greater than 40 years.
  • the subject's vesicle stimulating hormone (FSH) level was converted into a four-category variable, that is, the subject's vesicle stimulating hormone (FSH) level was divided into four groups, namely: the subject's vesicle stimulating hormone (FSH) level was less than 6.5 IU/L, the subject's vesicle-stimulating hormone (FSH) level is 6.5 and above IU/L and less than 8.5IU/L, and the subject's vesicle-stimulating hormone (FSH) level is 8.5 IU/L and above and less than 10.5 IU/L, and the subject's vesicle-stimulating hormone (FSH) level was 10.5 IU/L and above.
  • the subjects' anti-Mullerian hormone (AMH) levels were converted into five-category variables, that is, subjects' anti-Mullerian hormone (AMH) levels were divided into five groups, namely: subjects' anti-Mullerian hormone (AMH) levels Anti-Mullerian hormone (AMH) level of less than 0.5ng/ml, subject's anti-Mullerian hormone (AMH) level of 0.5ng/ml and above and less than 1ng/ml, subject's anti-Mullerian hormone (AMH) level (AMH) level of 1 ng/ml and above and less than 1.5 ng/ml, the subject's anti-Mullerian hormone (AMH) level is 1.5 ng/ml and above and less than 2 ng/ml, and the subject's anti-Mullerian hormone (AMH) level is 1.5 ng/ml and above and less than 2 ng/ml Mueller's hormone (AMH) level is greater than or equal to 2ng/ml.
  • AMH
  • the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's follicle-stimulating hormone ( The FSH) level data were converted into a formula for predicting the subject's current probability of low ovarian response (p 0 ) by fitting a multi-category variable to calculate.
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • b, c, and The value of d is brought into formula 1 for calculation.
  • age, FSH, and AMH are 0 or 1.
  • a is any value selected from -4.072 to -3.188, and a is preferably -3.630; when the age of the subject is 30 years old and below, age is 0, and when the age of the subject is greater than 30 years old Age is 40 years old and below, age is 1, b is any value selected from 0.163 to 0.960, b is preferably 0.561, and when the age of the subject is greater than 40 years old, age is 1, b is selected from 0.295 Any value in ⁇ 1.317, b is preferably 0.806; when the subject's vesicle-stimulating hormone (FSH) level is less than 6.5IU/L, FSH is 0, and when the subject's vesicle-stimulating hormone (FSH) level is 6.5IU /L and above and less than 8.5IU
  • the AMH When the subject's anti-Mullerian hormone (AMH) level is 2ng/ml and above, the AMH is 0; when the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, the AMH is 0 is 1, d is any value selected from 2.708-3.701, d is preferably 3.204, when the subject's anti-Mullerian hormone (AMH) level is 0.5ng/ml and above and less than 1ng/ml, AMH is 1, d is any value selected from 1.985 ⁇ 2.887, d is preferably 2.436, when the anti-Mullerian hormone (AMH) level of the subject is 1ng/ml and above and less than 1.5ng/ml, AMH is 1, d is any value selected from 1.153 to 2.070, d is preferably 1.612, when the subject's anti-Mullerian hormone (AMH) level is 1.5ng/ml and above and less than 2ng/ml, AMH is 1,
  • the current probability of low ovarian response (p 0 ) of the subject can be calculated first.
  • the data collected in the data acquisition module that is, the age of the subject, the level of anti-Mullerian hormone (AMH), and the level of follicle-stimulating hormone (FSH), and then use the subject's age, anti-Mullerian duct
  • the cutoff points of hormone (AMH) level and follicle-stimulating hormone (FSH) level were used to transform the data of the subject's age, anti-Mullerian hormone (AMH) level, and follicle-stimulating hormone (FSH) level. It is converted into three-category or four-category or five-category variables.
  • age, anti-Mullerian hormone (AMH) level, and follicle-stimulating hormone (FSH) level obtained in the above formula 1 and the data acquisition module or step, age, anti-Mullerian hormone (AMH) Levels, follicle stimulating hormone (FSH) levels, were converted into a multi-categorical variable to calculate the subject's current probability of low ovarian response (p 0 ) using the multi-categorical variable as a predictor variable.
  • the subject is determined to be DOR, that is, the subject is predicted to be a low ovarian responder.
  • the inventors of this application are based on the published article (Xu et al. Journal of assisted reproduction and genetic. 2020.37:963-972), that is, the different ovarian reserve obtained by using the previous four-parameter ovarian reserve assessment model is related to Fertility relationships to look for specific ovarian reserve profiles (ie, probability of low ovarian response) where changes in ovarian reserve lead to changes in fertility. That is, cluster analysis is used to classify the population according to the predicted low response probability (or predicted DOR probability), and it is divided into four categories.
  • the actual diagnosis results of the ovarian reserve of these subjects are also summarized, and the actual clinical pregnancy rate and live birth rate of each group's initiation cycle or embryo transfer cycle are statistically analyzed to show the changes in fertility in each group.
  • the predicted data and the clinical data are summarized in Table 5 below. Based on Table 5, it can be seen that if the probability of low ovarian response of the subject is greater than or equal to 50%, that is, the clinical pregnancy rate and live birth rate of the D group population will decrease. If the probability of low ovarian reserve of the subject is greater than or equal to 25% , that is, the clinical pregnancy rate of the start-up cycle of the group C population also decreased, indicating that the fertility decline began. This is why the inventors of the present application have uniquely selected two points, 25% and 50%, to predict the low response probability from so many data. From this, it can be judged that the probability of low ovarian response can effectively assess the fertility level of the population.
  • the inventors of the present application assessed the current ovarian reserve of 16,820 subjects according to the ovarian reserve assessment model (i.e., the three-index assessment model) described in the present application, and established the DOR ratio and age according to the DOR ratio of each age group.
  • age is an important factor in the occurrence of DOR.
  • the internationally recognized "Fixinterval" hypothesis believes that the change trend of the population's ovarian reserve function (predicted probability of low ovarian response or DOR probability/proportion) with age can actually reflect the change trend of individual ovarian reserve (DOR ratio) with age. Therefore, the inventor of the present application uses a logistic curve to fit the age and the DOR probability/ratio, so as to realize the prediction of the number of years in which the ovarian reserve of the subject will decline to a certain extent.
  • the present application can further calculate the number of years in which the subject's ovarian reserve has declined to a certain extent, which is based on the subject's current ovarian reserve, that is, the probability of low ovarian response (p 0 ) to calculate the number of years in which subjects experienced new changes in ovarian reserve.
  • the following formula 3 is used to calculate the subject from the current ovarian reserve (p 0 ) to the obvious decline of the ovarian reserve, that is, the probability of low ovarian response is 50 % Years:
  • age2 represents the age at which the ovarian reserve of the subject is significantly decreased
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the following formula 4 is used to calculate the subject's current ovarian reserve (p 0 ) to the near-depletion of ovarian reserve, that is, the probability of low ovarian response is: 95% of the year:
  • age3 represents the age at which the ovarian reserve of the subject is close to depletion
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the following formula 5 is used to calculate the subject from the current ovarian reserve (p 0 ) to the decline of the ovarian reserve, that is, the probability of low ovarian response is 25% Years of:
  • age4 represents the age at which the ovarian reserve of the subject begins to decline
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • the current ovarian reserve (p 0 ) of any subject can be calculated first, and then can be calculated using the above-mentioned formula three, formula four or formula five.
  • the subject went from current ovarian reserve (p 0 ) to a decline in ovarian reserve to some extent.
  • the applicant of the present application received the subjects who received the above-mentioned GnRH antagonist treatment between 2017 and 2018, of which the data of 1523 subjects in 2017 finally met the above-mentioned criteria. Incorporated into this example, data from 3273 subjects in 2018 that met the above criteria were included in this example. Used to build the system involved in this application.
  • Inclusion criteria were: women between the ages of 20 and 45, body mass index (BMI) ⁇ 30, six consecutive menstrual cycles of 25 to 45 days, normal bilateral ovarian morphology as assessed by transvaginal ultrasonography, previous IVF/ICSI- The number of ET cycles is less than or equal to 2.
  • Exclusion criteria were: hydrosalpinx, unilateral ovarian AFC>20, polycystic ovary syndrome, other untreated metabolic or endocrine diseases, previous surgery on the ovary or uterine cavity, intrauterine abnormalities, within 3 months of pregnancy, Smoking, use of oral contraceptives or other hormones in the previous two months, previous radiation or chemotherapy, and PGD (preimplantation genetic diagnosis)/PGS (preimplantation genetic screening) genes Diagnosed couple.
  • COS Controlled ovarian stimulation
  • Gn ie, human recombinant FSH treatment was initiated on day 2 or 3 of the menstrual cycle.
  • the starting dose is based on age, BMI (that is, body mass index, which is a number obtained by dividing the weight in kilograms by the square of the height in meters, which is a standard commonly used internationally to measure the degree of fatness and thinness of the human body and whether it is healthy), menstruation 2 -4 days FSH and AFC levels to choose from.
  • BMI body mass index
  • FSH -4 days FSH
  • AFC menstruation 2 -4 days FSH and AFC levels to choose from.
  • Gn initial dose is adjusted according to ultrasonography and serum E 2 levels.
  • GnRH antagonist treatment began on days 5-7 of stimulation, when growing follicles were 10-12 mm in diameter.
  • hCG When at least 2 dominant follicles ( ⁇ 18 mm in diameter) were visible by ultrasound, 5000-10000 IU of hCG was administered to initiate final oocyte maturation. Egg retrieval was performed 36 hours after hCG administration. Transfer 1-3 embryos or perform embryo cryopreservation. Luteal phase progesterone support was then provided.
  • FSH follicle stimulating hormone
  • AMH anti-Mullerian hormone
  • the follicle-stimulating hormone (FSH) level at 2-4 days of menstruation refers to the level of follicle-stimulating hormone obtained by detecting venous blood serum samples of female subjects on the second to fourth day of menstruation.
  • the AMH level on any day of the menstrual cycle refers to the anti-Mullerian hormone level measured in venous blood serum samples from female subjects on any day of the menstrual cycle.
  • Table 1 The data of the system used to build the model is shown in Table 1 below.
  • the above 4796 subjects with poor ovarian response and less than 5 (specifically 0, 1, 2, 3 or 4) oocytes were defined as outcome variables, predicting The variables were age, FSH level and AMH level.
  • the prediction model is constructed using the data of 2017, that is, the data of 1523 subjects is used to initially construct the model system of this application, and the data of 2018, that is, the data of 3273 subjects is used. to verify the effect of the system model.
  • the specific steps are to use JMP Pro 14.2 software, firstly apply multivariate logistic regression in the modeling data to build a prediction model of poor ovarian response, and verify the effect of the model in the validation data.
  • the performance of established predictive models was assessed using measures of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) provided in the software.
  • multivariate logistic regression was performed in the modeling data, that is, the data of 1523 subjects, with whether or not ovarian hyporesponsiveness was used as the outcome variable, and AMH, FSH and age were used as independent variables. Correlations, therefore, transformed the three continuous variables into categorical variables, and the grouping criteria for the three parameters age, FSH level and AMH level are shown in Table 2.
  • Subject age, AMH, and FSH were transformed into multi-categorical variables according to the groupings identified in Table 2.
  • the age of the subjects was divided into three groups, namely: the age of the subjects was under 30 years old, the age of the subjects was more than 30 years old and under 40 years old, and the age of the subjects was more than 40 years old.
  • the subjects' anti-Mullerian hormone (AMH) levels were divided into five groups, namely: subjects whose anti-Mullerian hormone (AMH) levels were less than 0.5 ng/ml, subjects whose anti-Mullerian hormone (AMH) levels were less than 0.5 ng/ml
  • the level of AMH is more than 0.5ng/ml and less than 1ng/ml
  • the level of anti-Mullerian hormone (AMH) of the subject is more than 1ng/ml and less than 1.5ng/ml
  • the anti-Mullerian hormone (AMH) level of the subject is more than 1 ng/ml and less than 1.5ng/ml.
  • the subjects' FSH levels were divided into four groups, namely: subjects whose FSH levels were less than 6.5IU/L, and subjects whose FSH levels were 6.5 IU/L IU/L or more and less than 8.5IU/L, the subject's vesicle-stimulating hormone (FSH) level is more than 8.5IU/L and less than 10.5IU/L, and the subject's vesicle-stimulating hormone (FSH) level is 10.5IU /L above, thereby transforming age, AMH, and FSH into multi-categorical variables according to the above criteria.
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject, wherein a, b, c and d are unitless parameters; wherein, in the calculation of ovarian reserve function In the module, the values of b, c and d are obtained based on the subject's age, the subject's anti-Mullerian hormone (AMH) level, and the subject's vesicle-stimulating hormone (FSH) level to bring into the formula
  • age, FSH, and AMH take the values 0 or 1 in the calculation.
  • a is any value selected from -4.072 to -3.188, a is preferably -3.630; when the age of the subject is 30 years old and below, age is 0 , when the subject's age is greater than 30 years old and 40 years old and below, age is 1, b is any value selected from 0.163 to 0.960, b is preferably 0.561, and when the subject's age is greater than 40 years old , age is 1, b is any value selected from 0.295 to 1.317, b is preferably 0.806; when the subject's vesicular stimulating hormone (FSH) level is less than 6.5IU/L, FSH is 0, and when the subject's vesicle stimulating hormone (FSH) level is less than 6.5IU/L When the level of bubble stimulating hormone (FSH) is 6.5IU/L and above and less than 8.5IU/L, FSH is 1, c is any value selected from 0.239 to 1.006, c is preferably
  • the AMH When the subject's anti-Mullerian hormone (AMH) level is 2ng/ml and above, the AMH is 0; when the subject's anti-Mullerian hormone (AMH) level is less than 0.5ng/ml, the AMH is 0 is 1, d is any value selected from 2.708-3.701, d is preferably 3.204, when the subject's anti-Mullerian hormone (AMH) level is 0.5ng/ml and above and less than 1ng/ml, AMH is 1, d is any value selected from 1.985 ⁇ 2.887, d is preferably 2.436, when the anti-Mullerian hormone (AMH) level of the subject is 1ng/ml and above and less than 1.5ng/ml, AMH is 1, d is any value selected from 1.153 to 2.070, d is preferably 1.612, when the subject's anti-Mullerian hormone (AMH) level is 1.5ng/ml and above and less than 2ng/ml, AMH is 1,
  • Equation 1 a subject's age, anti-Mullerian hormone concentration on any day of the menstrual cycle, and follicle-stimulating hormone concentration in venous blood on days 2-4 of the menstrual cycle can be calculated for this subject The probability of low ovarian response in patients.
  • the inventors of the present invention used three indicators of serum AMH level on any day of the menstrual cycle, age and serum FSH level on 2-4 days of menstruation to evaluate ovarian reserve function for the first time.
  • the antral follicle count (AFC) counted by transvaginal sonography can no longer be used, but the accuracy is still the same as that of the previous system. ), which can reduce the detection cost.
  • AFC antral follicle count
  • the accuracy and repeatability of FSH and AMH results are better.
  • Using the three-index system of the present application can achieve similar effects to the four-index system.
  • the system and method of the present invention can quickly and accurately evaluate the ovarian reserve level of a subject, and solve the problem of evaluating ovarian reserve function mainly based on doctor's experience and some simple cut-point values of ovarian reserve indicators in the prior art. There are problems of poor repeatability and inconsistent standards.
  • IVF/ICSI-ET in vitro fertilization/intracytoplasmic sperm injection-embryo transfer
  • the inventors collected the medical records of the subjects, obtained information including age, body mass index (BMI), duration of infertility, number of previous IVF/ICSI-ET attempts, serum basal estradiol (E 2 ), basal luteinizing hormone (LH), follicle-stimulating hormone (FSH) and anti-Mullerian hormone (AMH) levels, antral follicle count (AFC) of left and right ovaries, and records of causes of infertility, etc.
  • BMI body mass index
  • E 2 serum basal estradiol
  • LH basal luteinizing hormone
  • FSH follicle-stimulating hormone
  • AMH anti-Mullerian hormone
  • AFC antral follicle count
  • the criterion for judging a subject as DOR is that the predicted probability of low ovarian response calculated by the previous ovarian reserve assessment model is greater than or equal to 50%, and whether the subject is DOR is used as the outcome variable for model construction.
  • the D group population is the predicted DOR population, as shown in Table 5 below.
  • the inventors of the present application define the DOR population as females whose predicted probability of low ovarian response exceeds 50%, that is, p 0 is greater than or equal to 0.5. In this example, all subjects can be divided into non-DOR group and DOR group based on this model.
  • Table 5 shows the clinical pregnancy rate and live birth rate for each group after dividing these subjects into 4 groups.
  • the DOR population in group D was higher than that in groups A and B in terms of clinical pregnancy rate and live birth rate per initial cycle or clinical pregnancy rate and live birth rate per transplant cycle, indicating a significant decline in fertility. Therefore, it is suggested that the population should try to conceive as soon as possible before entering the DOR.
  • the population can be divided into three groups based on pDOR, namely pDOR less than 0.25 group, pDOR greater than or equal to 0.25 less than 0.5 group, and pDOR greater than or equal to 0.5 less than 1 group.
  • x represents the growth rate. Based on Table 6, it can be seen that x is any value selected from 0.202-0.250, preferably x is 0.226, and y represents the inflection point value. Based on Table 6, it can be seen that y is the selected value. Any numerical value from 42.768 to 43.712, preferably y is 43.240.
  • formula 3 In order to further calculate the time when a subject develops to a decrease in ovarian reserve that leads to a significant decrease in fertility, that is, the time when the low response probability reaches 50%, the following formula, ie, formula 3, can be used for calculation.
  • age2 represents the age at which the ovarian reserve of the subject is significantly decreased
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • age3 represents the age at which the ovarian reserve of the subject is depleted
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • age4 represents the age at which the ovarian reserve of the subject begins to decline
  • age1 represents the current age of the subject
  • x is any value selected from 0.202-0.250, preferably x is 0.226.
  • Equation 4 and Equation 5 it is also possible to calculate the subject from the current probability of low ovarian response to the decline of the subject's ovarian reserve to the point where fertility begins to decline, that is, the probability of low ovarian response (p) increases to The number of years required for a subject to go from the current probability of low ovarian response to near-depletion of the subject's ovarian reserve leading to near-depletion of fertility, i.e.

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Abstract

一种用于预测受试者出现卵巢储备新变化年限的系统，包括：数据采集模块，用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；计算卵巢储备功能的模块，利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量来计算受试者的当前卵巢低反应概率(p 0)；计算受试者出现卵巢储备下降到某程度年限的模块，利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限。

Description

预测受试者出现卵巢储备新变化年限的系统和方法 技术领域
本发明涉及一种用于优化的评估受试者卵巢储备功能的系统，利用该系统可以评估受试者其自身的卵巢储备功能的情况，以及根据受试者当前的卵巢储备情况评估受试者达到某一特定的卵巢储备状态，比如卵巢储备接近耗竭，卵巢储备下降所致早期生育力下降，已经卵巢储备下降导致生育力显著下降所需的年限(或称预测不同个体达到上述卵巢储备状态的年龄)。
背景技术
世界各地有很大比例的妇女选择推迟生育第一个孩子的年龄，以寻求机会改善自身教育水平和自身劳动力价值。众所周知，女性生育力(建立临床妊娠的能力)会随着女性年龄的增长而降低。因此，由于世界范围内推迟生育导致了目前全球范围内不孕不育率的增加。
卵巢储备(Ovarian reserve，OR)是指：卵巢皮质内含有的原始卵泡数。它反映卵巢提供健康可成功受孕卵子的能力，是女性生育力最重要的评价指标。一般来说，原始卵泡数量越多质量也越好，受孕几率也越高。卵巢储备随着年龄的增长而降低，卵巢储备越好，其生育力越高。原始卵泡的数量在孕中期达到约6-7百万，之后一部分发生闭锁，在出生时约有1-2百万的原始卵泡。青春期开始时原始卵泡的数量在约300,000-500,000个，而绝经年龄时，原始卵泡的数量在1000个左右。然而，人群原始卵泡的数量是高度异质的，出生时从数万到数百万不等，这是导致女性自然绝经的年龄(ANM)变异很大的主要原因。生育力下降开始于绝经前的10年左右，这样生育力下降开始年龄的差异也很大。
但是，许多育龄妇女不知道卵巢储备不同人存在较大差异，即卵巢皮质中原始卵泡数的数量，出生时数万至数百万。在我们的临床实践中，我们发现一些女性在四十多岁时仍然保持良好的卵巢储备，而有些女性二十多岁却 面临着卵巢储备减少(Diminished ovarian reserve,DOR)甚至耗竭的命运。为了改善不孕的情况，以成功怀孕，越来越多的夫妻寻求辅助生殖治疗(ART)。但是，并非所有夫妇都会从辅助生殖治疗中受益，辅助生殖治疗在DOR患者或围绝经期妇女中的作用是非常有限的，因为此时他们的生育力已经显著下降或接近于耗竭，卵巢里卵泡数已经很少或者没有，对于这种情况国际上已经达成了共识，即哪怕使用昂贵的促排卵治疗，这类人的生育力也不能得到改善。
尽管卵泡的数量和质量随着年龄的增长而发生了深刻的变化，但卵巢衰老的过程尚未引起人们的足够重视。人们往往直到月经不规则或更年期时才发现自己生育力可能下降了，但当出现这些体征时，其生育力已经极低，已经无法通过辅助生育技术来提高生育力。卵巢储备下降所致的早期生育力下降通常发生得较早，但由于长期缺乏明确的卵巢储备评估手段，导致大量育龄女性丧失了最佳的生育时机。
发明内容
本发明人之前的专利申请中，提供了一种用于评估受试者卵巢储备功能的系统，其包括：数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的数据；以及计算卵巢储备功能的模块，其用于将数据采集模块中的获取的上述信息进行计算，从而计算出受试者的卵巢低反应的概率(p)。在该系统中利用受试者工作特征(ROC)曲线来检测年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)的分界点，并根据该分界点的切点值来将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平、窦卵泡计数(AFC)转换成二分类变量，从而利用所述二分类变量作为预测变量来计算受试者的卵巢低反应概率(p)。
利用上述系统可以有效地计算受试者的卵巢低反应的概率，并且进一步，利用该系统包括的分组模块中预存有默认的卵巢储备功能分组参数，依据分组参数，对利用该系统计算得到的卵巢低反应概率p进行分组，从而可以实现对受试者的卵巢储备水平进行分组。
利用在此之前发明人开发的评估受试者卵巢储备功能的系统可以计算 受试者的卵巢低反应概率，并进一步依据该卵巢低反应的概率对受试者的卵巢储备水平进行分组。利用该系统可以计算出用于预测所述受试者的卵巢低反应概率的参数(p)，并依据系统预存的默认的卵巢储备功能分组参数，对该受试者的卵巢储备功能进行分组，从而判断其卵巢储备功能所处的水平，并对卵巢储备水平进行评估。
尽管已经开发了上述系统，但是由于窦卵泡计数(AFC)需要经过阴道超声学探查的方法计数双侧卵巢窦卵泡的总数，与年龄，以及通过抽血即可获得的AMH水平和FSH水平相比，获取较为困难，对受试者会造成一定伤害，取样困难，随着近年来AMH试剂盒的发展，由于AFC检测的复杂性、成本和进行检测的人员之间的差异，越来越多地建议使用AMH替代AFC来评估卵巢储备的情况。因此，本领域还需要进一步开发新的系统，希望可以用更为简单、便捷地检测数据来准确地预测受试者的卵巢储备功能。
在本专利申请中，首先提供了一种用于评估受试者卵巢储备功能的系统，其包括：数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；以及计算卵巢储备功能的模块，其用于将数据采集模块中的获取的上述信息进行计算，从而计算出受试者的卵巢低反应的概率(p)。在该系统中将受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平转换成多分类变量，从而利用所述多分类变量作为预测变量来计算受试者的卵巢低反应概率(p)。
利用在此发明人开发的评估受试者卵巢储备功能的系统可以计算受试者当前的卵巢低反应概率，并进一步依据该卵巢低反应的概率对受试者的卵巢储备水平进行分组。利用该系统可以计算出用于预测所述受试者的卵巢低反应概率的参数(p 0)。
进一步，国际上存在着‘Fixed interval’假说，即不同卵巢储备状态之间存在固定的时间间隔关系，也就是说卵泡消耗的速度人群中大致稳定，例如不同人经历从卵巢储备早期下降到卵巢储备下降明显再到卵巢储备耗竭的时间间隔大致相同。这种假设的主要根据就是人群的月经周期长度大致稳定，一般为28天。基于此假设，在本申请中，发明人因此推测描绘卵巢储备随年龄变化的时间间隔的增长曲线的曲线形状是相对固定的。
本申请的发明人根据这一假设，也就是说，在整个生育人群中，卵巢储 备状态随年龄的变化增长率是固定的，本申请的发明人尝试根据当前的卵巢储备情况，根据卵巢储备消耗的速度(随着年龄增长的DOR增长率)预测特定女性发展到卵巢储备早期下降(低反应概率为25％)的时间，以及进一步发展到卵巢储备下降导致生育力显著下降(低反应概率为50％)，或者发展到卵巢储备接近耗竭导致生育力接近耗竭(低反应概率为95％)的时间。希望开发出一套方法和系统来帮助女性根据其目前卵巢储备状况来预测达到预期卵巢储备状态的时间(或年龄)，从而对女性合理安排生育计划具有重要意义，这可能是降低育龄妇女不育率的有效方法。
具体来说，本申请涉及如下内容：
1.一种用于预测受试者出现卵巢储备新变化年限或年龄的系统，其包括：
数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；
计算卵巢储备功能的模块，利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量来计算受试者的当前卵巢低反应概率(p 0)，即当前的卵巢储备情况；
计算受试者出现卵巢储备下降到某程度年限的模块，其利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限。
2.根据权利要求1所述的系统，其中，所述抗缪勒氏管激素(AMH)水平是指女性受试者月经周期任意一天的静脉血中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经第2天的静脉血中的卵泡刺激素浓度。
3.根据权利要求1或2所述的系统，其中，
受试者出现卵巢储备下降到某程度是指以下三种情况：
受试者的卵巢储备下降到导致生育力开始下降，即卵巢低反应概率(p)升高到25％；
受试者的卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率(p)升高到50％；
受试者的卵巢储备接近耗竭导致生育力接近耗竭，即卵巢低反应概率(p)升高到95％。
4.根据权利要求3所述的系统，其中，
预测受试者的卵巢储备下降到导致生育力开始下降的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于25％所需的年限；
预测受试者的卵巢储备明显下降导致生育力明显下降的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于50％所需的年限；
预测受试者的卵巢储备接近耗竭导致生育力耗竭的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于95％所需的年限。
5.根据权利要求1所述的系统，其中，
在计算卵巢储备功能的模块中，将受试者年龄转换成三分类变量，
即将受试者的年龄分为三组，分别为：受试者的年龄在30岁及以下，受试者的年龄在大于30岁且在40岁及以下，以及受试者的年龄大于40岁。
6.根据权利要求1述的系统，其中，
在计算卵巢储备功能的模块中，将受试者的泡刺激素(FSH)水平转换成四分类变量，
即将受试者的泡刺激素(FSH)水平分为四组，分别为：受试者的泡刺激素(FSH)水平小于6.5IU/L，受试者的泡刺激素(FSH)水平在6.5及IU/L以上且小于8.5IU/L，受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L，以及受试者的泡刺激素(FSH)水平在10.5IU/L及以上。
7.根据权利要求1所述的系统，其中，
在计算卵巢储备功能的模块中，将受试者的抗缪勒氏管激素(AMH)水平转换成五分类变量，
即将受试者的抗缪勒氏管激素(AMH)水平分为五组，分别为：受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平 在1.5ng/ml及以上且小于2ng/ml，以及受试者的抗缪勒氏管激素(AMH)水平大于等于2ng/ml。
8.根据项1～7中任一项所述的系统，其中，
在计算卵巢储备功能的模块中，预先存储有基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、以及受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量拟合而成的用于预测受试者当前的卵巢低反应概率(p 0)的公式。
9.根据项8所述的系统，其中，
所述公式为如下公式一：
Figure PCTCN2020102088-appb-000001
其中，p 0为计算出的用于表征所述受试者当前的卵巢储备功能的参数，
其中，a、b、c和d为无单位参数；
其中，在计算卵巢储备功能的模块中，基于受试者的年龄、受试者的抗缪勒氏管激素(AMH)水平和受试者的泡刺激素(FSH)水平来获取b、c和d的取值来带入公式一进行计算，在计算中age，FSH，以及AMH取值为0或1。
10.根据项9所述的系统，其中，
a为选自-4.072～-3.188中的任意数值，a优选为-3.630；
当受试者的年龄在30岁及以下时，age为0，
当受试者的年龄在大于30岁且在40岁及以下，age为1，b为选自0.163～0.960中的任意数值，b优选为0.561，以及
当受试者的年龄大于40岁时，age为1，b为选自0.295～1.317中的任意数值，b优选为0.806；
当受试者的泡刺激素(FSH)水平小于6.5IU/L时，FSH为0，
当受试者的泡刺激素(FSH)水平在6.5IU/L及以上且小于8.5IU/L时，FSH为1，c为选自0.239～1.006中的任意数值，c优选为0.622，
当受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L时，FSH为1，c为选自0.363～1.303中的任意数值，c优选为0.833，以及
当受试者的泡刺激素(FSH)水平在10.5IU/L及以上时，FSH为1，c为选自0.847～1.712中的任意数值，c优选为1.279；
当受试者的抗缪勒氏管激素(AMH)水平在2ng/ml及以上时，AMH为0；
当受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml时，AMH为1，d为选自2.708～3.701中的任意数值，d优选为3.204，
当受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml时，AMH为1，d为选自1.985～2.887中的任意数值，d优选为2.436，
当受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml时，AMH为1，d为选自1.153～2.070中的任意数值，d优选为1.612，
当受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml时，AMH为1，d为选自0.230～1.356中的任意数值，d优选为0.793。
11.根据项8-10任一项所述的系统，其中，
在计算受试者出现卵巢储备下降到某程度年限的模块中，利用如下公式三来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率为50％的年限：
Figure PCTCN2020102088-appb-000002
其中，age2表示受试者出现卵巢储备明显下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
12.根据项8-10任一项所述的系统，其中，
在计算受试者出现卵巢储备下降到某程度年限的模块中，利用如下公式四来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备接近耗尽(生育力接近耗竭)，即卵巢低反应概率为95％的年限：
Figure PCTCN2020102088-appb-000003
其中，age3表示受试者卵巢储备接近耗竭的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
13.根据项8-10任一项所述的系统，其中，
在计算受试者出现卵巢储备下降到某程度年限的模块中，利用如下公式五来计算受试者从当前的卵巢储备情况(p 0)到卵巢储备下降导致生育力开始下降，即卵巢低反应概率为25％的年限：
Figure PCTCN2020102088-appb-000004
其中，age4表示受试者卵巢储备开始下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
14.一种用于预测受试者出现卵巢储备新变化年限或年龄的方法，其包括：
数据采集步骤，其获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；
计算卵巢储备功能，其利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量来计算受试者的当前卵巢低反应概率(p 0)，即当前的卵巢储备情况；
计算受试者出现卵巢储备下降到某程度年限的步骤，其利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限或年龄。
15.根据项14所述的方法，其中，所述抗缪勒氏管激素(AMH)水平是指女性受试者月经周期任意一天的静脉血中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经第2天的静脉血中的卵泡刺激素浓度。
16.根据项14或15所述的方法，其中，
受试者出现卵巢储备下降到某程度是指以下三种情况：
受试者的卵巢储备下降到导致生育力开始下降，即卵巢低反应概率(p)升高到25％；
受试者的卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率(p)升高到50％；
受试者的卵巢储备接近耗竭导致生育力接近耗竭，即卵巢低反应概率(p)升高到99％。
17.根据项16所述的方法，其中，
预测受试者的卵巢储备下降到导致生育力开始下降的年限是指：利用计算卵巢储备功能的步骤计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于25％所需的年限；
预测受试者的卵巢储备明显下降导致生育力明显下降的年限是指：利用计算卵巢储备功能的步骤计算受试者的当前卵巢储备，即当前的卵巢低反应 概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于50％所需的年限；
预测受试者的卵巢储备接近耗竭导致生育力耗竭的年限是指：利用计算卵巢储备功能的步骤计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于95％所需的年限。
18.根据项14所述的方法，其中，
在计算卵巢储备功能的步骤中，将受试者年龄转换成三分类变量，
即将受试者的年龄分为三组，分别为：受试者的年龄在30岁及以下，受试者的年龄在大于30岁且在40岁及以下，以及受试者的年龄大于40岁。
19.根据项14所述的方法，其中，
在计算卵巢储备功能的步骤中，将受试者的泡刺激素(FSH)水平转换成四分类变量，
即将受试者的泡刺激素(FSH)水平分为四组，分别为：受试者的泡刺激素(FSH)水平小于6.5IU/L，受试者的泡刺激素(FSH)水平在6.5及IU/L以上且小于8.5IU/L，受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L，以及受试者的泡刺激素(FSH)水平在10.5IU/L及以上。
20.根据项14所述的方法，其中，
在计算卵巢储备功能的步骤中，将受试者的抗缪勒氏管激素(AMH)水平转换成五分类变量，
即将受试者的抗缪勒氏管激素(AMH)水平分为五组，分别为：受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml，以及受试者的抗缪勒氏管激素(AMH)水平大于等于2ng/ml。
21.根据项14～20中任一项所述的方法，其中，
在计算卵巢储备功能的步骤中，预先存储有基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、以及受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量拟合而成的用于预测受试者当前卵巢 低反应概率(p 0)的公式。
22.根据项21所述的方法，其中，
所述公式为如下公式一：
Figure PCTCN2020102088-appb-000005
其中，p 0为计算出的用于表征所述受试者当前卵巢储备功能的参数，
其中，a、b、c和d为无单位参数；
其中，在计算卵巢储备功能的步骤中，基于受试者的年龄、受试者的抗缪勒氏管激素(AMH)水平和受试者的泡刺激素(FSH)水平来获取b、c和d的取值来带入公式一进行计算，在计算中age，FSH，以及AMH取值为0或1。
23.根据项22所述的方法，其中，
a为选自-4.072～-3.188中的任意数值，a优选为-3.630；
当受试者的年龄在30岁及以下时，age为0，
当受试者的年龄在大于30岁且在40岁及以下，age为1，b为选自0.163～0.960中的任意数值，b优选为0.561，以及
当受试者的年龄大于40岁时，age为1，b为选自0.295～1.317中的任意数值，b优选为0.806；
当受试者的泡刺激素(FSH)水平小于6.5IU/L时，FSH为0，
当受试者的泡刺激素(FSH)水平在6.5IU/L及以上且小于8.5IU/L时，FSH为1，c为选自0.239～1.006中的任意数值，c优选为0.622，
当受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L时，FSH为1，c为选自0.363～1.303中的任意数值，c优选为0.833，以及
当受试者的泡刺激素(FSH)水平在10.5IU/L及以上时，FSH为1，c为选自0.847～1.712中的任意数值，c优选为1.279；
当受试者的抗缪勒氏管激素(AMH)水平在2ng/ml及以上时，AMH为0；
当受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml时，AMH为1，d为选自2.708～3.701中的任意数值，d优选为3.204，
当受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml时，AMH为1，d为选自1.985～2.887中的任意数值，d优选为2.436，
当受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5 ng/ml时，AMH为1，d为选自1.153～2.070中的任意数值，d优选为1.612，
当受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml时，AMH为1，d为选自0.230～1.356中的任意数值，d优选为0.793。
24.根据项21-23任一项所述的方法，其中，
在计算受试者出现卵巢储备下降到某程度年限的步骤中，利用如下公式三来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率为50％的年限：
Figure PCTCN2020102088-appb-000006
其中，age2表示受试者出现卵巢储备明显下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
25.根据项21-23任一项所述的方法，其中，
在计算受试者出现卵巢储备下降到某程度年限的步骤中，利用如下公式四来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备接近耗尽(生育力接近耗竭)，即卵巢低反应概率为95％的年限：
Figure PCTCN2020102088-appb-000007
其中，age3表示受试者卵巢储备接近耗竭的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
26.根据项21-23任一项所述的方法，其中，
在计算受试者出现卵巢储备下降到某程度年限的步骤中，利用如下公式五来计算受试者从当前的卵巢储备情况(p 0)到卵巢储备下降导致生育力开始下降，即卵巢低反应概率为25％的年限：
Figure PCTCN2020102088-appb-000008
其中，age4表示受试者卵巢储备开始下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
发明效果
根据发明人的系统可以早期识别出卵巢储备功能降低(DOR)的这类人群。利用本申请的系统和方法，可以帮助受试者预测出其卵巢储备发生关键变化 的年限，并提示人群在进入DOR之前就应该尽早尝试生育。卵泡的数量和质量随着年龄的增长而发生深刻的变化，但卵巢衰老的过程尚未引起人们的足够重视。人们往往直到月经不规则或更年期时才发现自己生育力可能下降了，但当出现这些体征时，其生育力已经极低，已经无法通过辅助生育技术来提高生育力。卵巢储备下降所致的早期生育力下降通常发生得较早，但由于长期缺乏明确的卵巢储备评估手段，因此导致大量育龄女性丧失了最佳的生育时机，但如上所述，利用本申请的系统和方法，可以准确地帮助受试者预测会在什么时候出现卵巢储备开始下降导致生育力开始下降，会在什么时候出现卵巢储备明显下降导致生育力明显下降，或者会在什么时候卵巢储备接近耗竭导致生育力接近耗竭。利用这样的系统和方法可以及时帮助育龄女性了解其最佳的生育时机。
附图说明
通过阅读下文优选的具体实施方式中的详细描述，本申请各种其他的优点和益处对于本领域普通技术人员将变得清楚明了。说明书附图仅用于示出优选实施方式的目的，而并不认为是对本申请的限制。显而易见地，下面描述的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。而且在整个附图中，用相同的附图标记表示相同的部件。
图1实施例中各年龄段DOR的比例建立了DOR比例与年龄关系的逻辑曲线(生长曲线)图。
具体实施方式
下面将更详细地描述本发明的具体实施例。然而应当理解，可以以各种形式实现本发明而不应被这里阐述的实施例所限制。相反，提供这些实施例是为了能够更透彻地理解本发明，并且能够将本发明的范围完整的传达给本领域的技术人员。
需要说明的是，在说明书及权利要求当中使用了某些词汇来指称特定组件。本领域技术人员应可以理解，技术人员可能会用不同名词来称呼同一个组件。本说明书及权利要求并不以名词的差异来作为区分组件的方式，而是 以组件在功能上的差异来作为区分的准则。如在通篇说明书及权利要求当中所提及的“包含”或“包括”为一开放式用语，故应解释成“包含但不限定于”。说明书后续描述为实施本发明的较佳实施方式，然所述描述乃以说明书的一般原则为目的，并非用以限定本发明的范围。本发明的保护范围当视所附权利要求所界定者为准。
在本申请涉及卵巢储备是指：卵巢皮质内含有的原始卵泡数，称为卵巢储备。它反映卵巢提供健康可成功受孕卵子的能力，是女性卵巢功能的最重要的评价指标。一般来说，原始卵泡数量越多质量也越好，受孕几率也越高。
在本申请所称的卵巢低反应，也称为卵巢储备减少(Decreased ovarian reserve,DOR)是指在生殖周期的获卵日，获取的卵母细胞数量低于5(即0-4个)。
但是原始卵泡数没办法进行无创的评估，只能通过每个月经周期动员的卵泡数进行评估，IVF-ET周期动员的卵泡过少(卵巢低反应)，提示卵巢储备功能下降。
通常认为年龄因素是评价卵巢储备的最重要因素，一项关于年龄与IVF成功率的研究结果显示：30岁以下妇女IVF成功率约26％，而当年龄在37岁及以上时IVF成功率仅为9％。
在本申请中，根据之前的卵巢储备评估模型，如果预测卵巢低反应概率大于等于50％，则预测该受试者会出现低反应，则将该受试者诊断为DOR。
卵巢储备能力随年龄增长而下降的机制如下。(一)卵泡数量减少，原始卵泡出现于胚胎性别分化以后，此时卵泡数最多，青春期后卵泡开始发育成熟，随着排卵的完成大量被募集而未排出的卵泡萎缩消失形成黄体。卵泡数随着年龄增加而不断减少：人类中20周龄胚胎最多，约为600万个卵泡，新生儿期减少至70-200万，青春期约4万，绝经期开始时仅余千余，直至完全耗竭。(二)卵子质量下降，胚胎质量主要由卵子质量决定，大龄可致卵细胞非整倍体几率增加、线粒体功能异常风险增加、卵子极性消失和卵细胞表观遗传学改变。(三)内分泌因素，下丘脑-垂体-卵巢轴调节妇女月经周期和排卵，该轴内分泌水平异常会导致不孕。AMH和inhibin B由小卵泡分泌，是卵巢储备能力的直接体现。随着年龄的增长卵巢储备降低，可募集的卵泡数减少，因此其分泌的AMH和inhibin B浓度也随之下降。Inhibin B可负 反馈调节垂体FSH分泌，inhibin B水平下降导致黄体期FSH分泌增加。提前增加的FSH促进新卵泡的生长和E2分泌，最终缩短了月经周期。血清FSH水平增加，inhibin B水平下降，卵泡对FSH敏感度下降，提示可被募集的窦状卵泡数减少。月经周期是卵巢储备和生育力的体现，大龄致月经周期缩短，月经周期减少2-3天是生殖系统衰老的敏感指征，提示卵泡生长提前启动(FSH水平升高)，原始卵泡储备下降。
连续变量：在统计学中，变量按变量值是否连续可分为连续变量与分类变量两种。在一定区间内可以任意取值的变量叫连续变量，其数值是连续不断的，相邻两个数值可作无限分割，即可取无限个数值。例如，生产零件的规格尺寸，人体测量的身高、体重、胸围等为连续变量，其数值只能用测量或计量的方法取得。反之，其数值只能用自然数或整数单位计算的则为离散变量。例如，企业个数，职工人数，设备台数等，只能按计量单位数计数，这种变量的数值一般用计数方法取得。
分类变量是指地理位置、人口统计等方面的变量，其作用是将调查响应者分群。描述变量是描述某一个客户群与其他客户群的区别。大部分分类变量也就是描述变量。分类变量可以分为无序分类变量和有序分类变量两大类。其中，无序分类变量(unordered categorical variable)是指所分类别或属性之间无程度和顺序的差别。其又可分为①二项分类，如性别(男、女)，药物反应(阴性和阳性)等；②多项分类，如血型(O、A、B、AB)，职业(工、农、商、学、兵)等。而有序分类变量(ordinal categorical variable)各类别之间有程度的差别。如尿糖化验结果按－、±、+、++、+++分类；疗效按治愈、显效、好转、无效分类。对于有序分类变量，应先按等级顺序分组，清点各组的观察单位个数，编制有序变量(各等级)的频数表，所得资料称为等级资料。
变量类型不是一成不变的，根据研究目的的需要，各类变量之间可以进行转化。例如血红蛋白量(g/L)原属数值变量，若按血红蛋白正常与偏低分为两类时，可按二项分类资料分析；若按重度贫血、中度贫血、轻度贫血、正常、血红蛋白增高分为五个等级时，可按等级资料分析。有时亦可将分类资料数量化，如可将病人的恶心反应以0、1、2、3表示，则可按数值变量资料(定量资料)分析。
Logistic函数或Logistic曲线是一种S形函数，它是皮埃尔·弗朗索瓦·韦 吕勒在1844或1845年在研究它与人口增长的关系时命名的。广义Logistic曲线可以模仿一些情况人口增长(P)的S形曲线。起初阶段大致是指数增长；然后随着开始变得饱和，增加变慢；最后，达到成熟时增加停止。
本申请涉及一种用于预测受试者出现卵巢储备新变化年限或年龄的系统，其包括：数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；计算卵巢储备功能的模块，其利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量来计算受试者的当前卵巢低反应概率(p 0)，即当前的卵巢储备情况；计算受试者出现卵巢储备下降到某程度年限的模块，其利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限或年龄。
本申请涉及一种用于预测受试者出现卵巢储备新变化年限或年龄的方法，其包括：数据采集步骤，其获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；计算卵巢储备功能，其利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量来计算受试者的当前卵巢低反应概率(p 0)，即当前的卵巢储备情况；计算受试者出现卵巢储备下降到某程度年限的步骤，其利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限或年龄。
在本申请中，通常可以基于受试者当前的卵巢低反应概率(p 0)，利用本申请的方法或系统可以预测该受试者出现卵巢储备下降到某程度导致生育力出现相应变化需要的时间，即年限，或者受试者出现卵巢储备下降到某程度时的具体的年龄。
具体来说，在本申请的系统和方法中，所述抗缪勒氏管激素(AMH)水平是指女性受试者月经周期任意一天的静脉血中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经第2天的静脉血中的卵泡刺激素浓度。
其中，缪勒氏管激素(AMH)是一种由卵巢小卵泡的颗粒层细胞所分泌的荷尔蒙，胎儿时期的女宝宝从9个月大便开始制造AMH，卵巢内的小卵泡数量越多，AMH的浓度便越高；反之，当卵泡随着年龄及各种因素逐渐消 耗，AMH浓度也会随之降低，越接近更年期，AMH便渐趋于0。
卵泡刺激素(FSH)是垂体前叶嗜碱性细胞分泌的一种激素，成分为糖蛋白，主要作用为促进卵泡成熟。FSH可促进卵泡颗粒层细胞增生分化，并促进整个卵巢长大。而其作用于睾丸曲细精管则可促进精子形成。FSH在人体内呈脉冲式分泌，女性随月经周期而改变。测定血清中FSH对了解垂体内分泌功能，间接了解卵巢的功能状态、评估卵巢储备及卵巢反应性、制定促排卵用药剂量等不孕和内分泌疾病的诊断治疗都有重要的意义。
在本申请的系统和方法中，受试者出现卵巢储备下降到某程度是指以下三种情况：受试者的卵巢储备下降到导致生育力开始下降，即卵巢低反应概率(p)升高到25％；受试者的卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率(p)升高到50％；受试者的卵巢储备接近耗竭导致生育力接近耗竭，即卵巢低反应概率(p)升高到95％。
在本申请的系统和方法中，预测受试者的卵巢储备下降到导致生育力开始下降的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于25％所需的年限；预测受试者的卵巢储备明显下降导致生育力明显下降的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于50％所需的年限；预测受试者的卵巢储备接近耗竭导致生育力耗竭的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于95％所需的年限。
在本申请的系统和方法中，如上所述，需要计算卵巢储备功能，其利用受试者年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平转换成多分类变量，其中受试者年龄转换成三分类变量，即将受试者的年龄分为三组，分别为：受试者的年龄在30岁及以下，受试者的年龄在大于30岁且在40岁及以下，以及受试者的年龄大于40岁。受试者的泡刺激素(FSH)水平转换成四分类变量，即将受试者的泡刺激素(FSH)水平分为四组，分别为：受试者的泡刺激素(FSH)水平小于6.5IU/L，受试者的泡刺激素(FSH)水平在6.5及IU/L以上且小于8.5IU/L，受试者的泡刺激素(FSH)水平在8.5IU/L及以 上且小于10.5IU/L，以及受试者的泡刺激素(FSH)水平在10.5IU/L及以上。受试者的抗缪勒氏管激素(AMH)水平转换成五分类变量，即将受试者的抗缪勒氏管激素(AMH)水平分为五组，分别为：受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml，以及受试者的抗缪勒氏管激素(AMH)水平大于等于2ng/ml。
在本申请计算卵巢储备功能的模块或步骤中，利用基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平、以及受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量拟合而成的用于预测受试者当前的卵巢低反应概率(p 0)的公式来进行计算。
用于预测受试者当前的卵巢低反应概率(p 0)的公式为如下公式一：
Figure PCTCN2020102088-appb-000009
其中，p 0为计算出的用于表征所述受试者当前的卵巢储备功能的参数，
其中，a、b、c和d为无单位参数；
其中，在计算卵巢储备功能的模块中，基于受试者的年龄、受试者的抗缪勒氏管激素(AMH)水平和受试者的泡刺激素(FSH)水平来获取b、c和d的取值来带入公式一进行计算，在计算中age，FSH，以及AMH取值为0或1。进一步来说，a为选自-4.072～-3.188中的任意数值，a优选为-3.630；当受试者的年龄在30岁及以下时，age为0，当受试者的年龄在大于30岁且在40岁及以下，age为1，b为选自0.163～0.960中的任意数值，b优选为0.561，以及当受试者的年龄大于40岁时，age为1，b为选自0.295～1.317中的任意数值，b优选为0.806；当受试者的泡刺激素(FSH)水平小于6.5IU/L时，FSH为0，当受试者的泡刺激素(FSH)水平在6.5IU/L及以上且小于8.5IU/L时，FSH为1，c为选自0.239～1.006中的任意数值，c优选为0.622，当受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L时，FSH为1，c为选自0.363～1.303中的任意数值，c优选为0.833，以及当受试者的泡刺激素(FSH)水平在10.5IU/L及以上时，FSH为1，c为选自0.847～1.712中的 任意数值，c优选为1.279。当受试者的抗缪勒氏管激素(AMH)水平在2ng/ml及以上时，AMH为0；当受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml时，AMH为1，d为选自2.708～3.701中的任意数值，d优选为3.204，当受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml时，AMH为1，d为选自1.985～2.887中的任意数值，d优选为2.436，当受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml时，AMH为1，d为选自1.153～2.070中的任意数值，d优选为1.612，当受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml时，AMH为1，d为选自0.230～1.356中的任意数值，d优选为0.793。
如上所述，在本申请的方法和系统中，可以首先对受试者当前的卵巢低反应概率(p 0)来进行计算，具体来说，对于任意一个受试者，基于利用数据采集步骤或在数据采集模块中采集的数据，即获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据，然后利用受试者年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的分界点来对该受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据进行变量转换，将其转换成三分类或四分类或五分类变量。
基于如上公式一和数据采集模块或步骤中获取的受试者年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平，并将年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平转换成多分类变量，从而利用所述多分类变量作为预测变量来计算受试者的当前卵巢低反应概率(p 0)。
进一步，在本申请中，如果预测的受试者的当前卵巢低反应概率(p 0)大于等于50％，则判定该受试者为DOR，即预测该受试者为卵巢低反应者。
如上所述，对于任何一个受试者，当计算出其当前的卵巢储备情况(p 0)，均可以对该受试者卵巢储备未来达到某种卵巢储备变化所需时间进行计算。
在本申请中，本申请的发明人基于已发表的文章(Xu et al.Journal of assisted reproduction and genetic.2020.37:963-972)，即利用之前四参数卵巢储备评估模型得到的不同卵巢储备情况与生育力的关系，以寻找卵巢储备变化导致生育力变化的特定卵巢储备情况(即卵巢低反应概率)。即利用聚类分析按照预测低反应概率(或称预测DOR概率)对人群进行分类，一共分成四类。同时对这些受试者的卵巢储备情况的实际诊断结果也进行总结，对每组人的 启动周期或者胚胎移植周期的实际临床妊娠率和活产率进行统计分析，以显示各组生育力的变化，预测的数据和临床的数据总结的结果如下表5所示。基于表5可以看出，如果受试者的卵巢低反应概率大于等于50％，即D组人群的临床妊娠率和活产率均下降，如果受试者的卵巢储备低反应概率大于等于25％，即C组人群的启动周期临床妊娠率也出现下降，说明生育力下降开始。这就是本申请的发明人为什么从这么多数据中独创地选择预测低反应概率25％和50％这两个点。由此，可以判断出，卵巢低反应概率可以有效地评估人群的生育水平。
进一步本申请的发明人根据本申请中描述的卵巢储备评估模型(即三指标评估模型)，评估16820名受试者的当前的卵巢储备情况，根据各年龄段DOR的比例建立了DOR比例与年龄关系的逻辑曲线图(生长曲线)，如图1所示。根据图1可以看出，年龄是发生DOR的重要因素。国际上公认的“Fixinterval”假说认为人群的卵巢储备功能(预测的卵巢低反应概率或称DOR概率/比例)随年龄的变化趋势实际上可以体现个体卵巢储备(DOR比例)随年龄的变化趋势。从而本申请的发明人利用逻辑曲线对年龄与DOR概率/比例进行了拟合，从而实现了对受试者出现卵巢储备下降到某程度的年限进行了预测。
如上所述，本申请的基于受试者当前卵巢低反应概率(p 0)，可以进一步计算受试者出现卵巢储备下降到某程度年限，是利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限。
在计算受试者出现卵巢储备下降到某程度年限的步骤中，利用如下公式三来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备明显下降，即卵巢低反应概率为50％的年限：
Figure PCTCN2020102088-appb-000010
其中，age2表示受试者出现卵巢储备明显下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
在计算受试者出现卵巢储备下降到某程度年限的步骤中，利用如下公式四来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备接近耗尽，即卵巢低反应概率为95％的年限：
Figure PCTCN2020102088-appb-000011
其中，age3表示受试者卵巢储备接近耗竭的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
在计算受试者出现卵巢储备下降到某程度年限的步骤中，利用如下公式五来计算受试者从当前的卵巢储备情况(p 0)到卵巢储备开始下降，即卵巢低反应概率为25％的年限：
Figure PCTCN2020102088-appb-000012
其中，age4表示受试者卵巢储备开始下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
如上所述，利用本申请所述的方法和系统，可以首先计算出任意受试者当前的卵巢储备情况(p 0)，然后进而可以利用如上所述的公式三、公式四或公式五来计算该受试者从当前的卵巢储备情况(p 0)到卵巢储备下降到某程度年限。利用本申请的方法和系统，根据卵巢储备消耗的速度(随着年龄增长的累积DOR增长率)预测特定女性发展到卵巢储备下降导致生育力早期下降(低反应概率为25％)的时间，以及进一步发展到卵巢储备下降导致生育力显著下降(低反应概率为50％)，或者发展到卵巢储备接近耗竭导致生育力接近耗竭(低反应概率为95％)的时间，从而帮助女性根据其目前卵巢储备状况来预测达到预期卵巢储备状态的时间(或年龄)，从而对女性合理安排生育计划具有重要意义，是降低育龄妇女不育率的有效方法。
实施例
构建优化的三指标卵巢储备评估系统模型
在本申请的实施例中，利用本申请的申请人在2017年和2018年之间接收了上述GnRH拮抗剂治疗的受试者，其中最终2017年有1523名受试者的数据符合上述标准被纳入到本实施例中，在2018年有3273名受试者的数据符合上述标准被纳入到本实施例中。用于构建本申请涉及的系统。
按照下述纳入和排除标准选择病例用于后续的研究。
纳入标准为：年龄在20～45岁之间的女性，体重指数(BMI)≤30，连续六个月经周期为25至45天，通过阴道超声检查评估双侧卵巢形态正常，既 往IVF/ICSI-ET周期数≤2。
排除标准为：输卵管积水，单侧卵巢AFC>20，多囊卵巢综合征，其他未经治疗的代谢或内分泌疾病，针对卵巢或宫腔的既往手术，宫内异常，妊娠3个月以内，吸烟，在之前的两个月内使用口服避孕药或其它激素，之前经历过放疗或化疗，接受PGD(植入前胚胎遗传学诊断)/PGS(胚胎植入前遗传学筛查)治疗的基因诊断的夫妇。
控制性卵巢刺激(COS)治疗
在月经周期的第2天或第3天开始给予Gn(即人重组FSH)治疗。起始剂量根据年龄、BMI(即身体质量指数，是用体重公斤数除以身高米数平方得出的数字，是目前国际上常用的衡量人体胖瘦程度以及是否健康的一个标准)、月经2-4天FSH和AFC水平来选择。在促排卵期间，Gn起始剂量根据超声观察和血清E 2水平来调整。GnRH拮抗剂治疗开始于刺激第5-7天，生长的卵泡直径为10-12mm时。当通过超声可见至少2个优势卵泡(直径≥18mm)时，给予5000-10000IU的hCG以引发最终的卵母细胞成熟。hCG给药36小时后进行取卵。移植1-3个胚胎或进行胚胎冷冻保存。然后提供了黄体期黄体酮支持物。
获取样品和内分泌测定
针对如上所述的4796名受试者，抽取静脉血样品并立即倒转五次以促进彻底的血液凝结，通过离心收集血清并用于内分泌评估。在受试者的月经周期第2天测量受试者的卵泡刺激素(FSH)水平，并在受试者的月经周期的任何一天测量受试者抗缪勒氏管激素(AMH)水平。使用西门子Immulite 2000免疫分析系统(西门子医疗诊断有限公司，上海，中国)进行血清的FSH测量。FSH测定的质量控制由Bio-RAD实验室提供(Lyphochek Immunoassay Plus Control，Trilevel，目录号370，批号40340)。使用超灵敏两点ELISA试剂盒(Ansh Labs，美国)检测受试者的血清AMH浓度。
在本实施例中，月经2-4天时的卵泡刺激素(FSH)水平是指对处于经期第二天～第四天的女性受试者的静脉血血清样本进行检测得到的卵泡刺激素水平。月经周期任何一天的AMH水平是指对处于经期中任一天女性受试者的静脉血血清样本进行检测得到的抗缪勒氏管激素水平。用于构建模型的系 统的数据情况如下表1所示。
表1进行GnRH拮抗剂治疗的受试者的临床和生化数据
  2017(n＝1523) 2018(n＝3273)
平均年龄(岁) 33.4±5.3 32.7±4.8
平均FSH(IU/L) 7.5±3.3 7.2±3.1
平均AMH(ng/ml) 2.2(1.1-4.0) 2.7(1.2-4.8)
系统模型构建
在本实施例中，将上述4796名受试者的卵巢反应差且受试者的卵母细胞少于5(具体来说为0、1、2、3或4)个定义为结果变量，预测变量为年龄，FSH水平和AMH水平。其中，在本实施例中预测模型使用2017年的数据构建的，即利用了1523名受试者的数据来初步构建本申请的模型系统，利用2018年的数据，即3273名受试者的数据来验证系统模型的效果。
具体步骤为利用JMP Pro 14.2软件，首先在建模数据中应用多因素逻辑回归，以构建卵巢反应不良的预测模型，并在验证数据中验证模型的效果。利用软件中提供的曲线下面积(AUC)、敏感性、特异性、正预测值(PPV)和负预测值(NPV)的测量来评估已建立的预测模型的性能。
首先在建模数据，即1523名受试者的数据中进行多因素逻辑回归，以是否卵巢低反应作为结局变量，以AMH，FSH和年龄作为自变量，由于三个自变量间具有较强的相关性，因此，将三个连续性变量转换为分类变量，三个参数年龄、FSH水平和AMH水平的分组标准，如表2所示。
表2分组依据
Figure PCTCN2020102088-appb-000013
依据表2确认的分组已经，将受试者年龄、AMH和FSH转换成多分类变量。将受试者的年龄分为三组，分别为：受试者的年龄在30岁以下，受试者的年龄在大于30岁且在40岁以下，以及受试者的年龄大于40岁。将受试者的抗缪勒氏管激素(AMH)水平分为五组，分别为：受试者的抗缪勒氏 管激素(AMH)水平小于0.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml以上且小于1ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1ng/ml以上且小于1.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml以上且小于2ng/ml，以及受试者的抗缪勒氏管激素(AMH)水平大于2ng/ml。将受试者的泡刺激素(FSH)水平分为四组，分别为：受试者的泡刺激素(FSH)水平小于6.5IU/L，受试者的泡刺激素(FSH)水平在6.5IU/L以上且小于8.5IU/L，受试者的泡刺激素(FSH)水平在8.5IU/L以上且小于10.5IU/L，以及受试者的泡刺激素(FSH)水平在10.5IU/L以上，从而依据上述标准将年龄、AMH和FSH转换成多分类变量。
同时利用上述训练组的数据拟合了如下公式和确认了公式中涉及的参数，如表3所示：
Figure PCTCN2020102088-appb-000014
表3
Figure PCTCN2020102088-appb-000015
如公式一所示p 0为计算出的用于表征所述受试者的当前的卵巢储备功能的参数，其中，a、b、c和d为无单位参数；其中，在计算卵巢储备功能的模块中，基于受试者的年龄、受试者的抗缪勒氏管激素(AMH)水平和受试者的泡刺激素(FSH)水平来获取b、c和d的取值来带入公式一进行计算，在计算中age，FSH，以及AMH取值为0或1。
如表3所示，公式一种涉及的参数为：a为选自-4.072～-3.188中的任意数值，a优选为-3.630；当受试者的年龄在30岁及以下时，age为0，当受试者的年龄在大于30岁且在40岁及以下，age为1，b为选自0.163～0.960中的任意数值，b优选为0.561，以及当受试者的年龄大于40岁时，age为1，b为选自0.295～1.317中的任意数值，b优选为0.806；当受试者的泡刺激素(FSH)水平小于6.5IU/L时，FSH为0，当受试者的泡刺激素(FSH)水平在6.5IU/L及以上且小于8.5IU/L时，FSH为1，c为选自0.239～1.006中的任意数值，c优选为0.622，当受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L时，FSH为1，c为选自0.363～1.303中的任意数值，c优选为0.833，以及当受试者的泡刺激素(FSH)水平在10.5IU/L及以上时，FSH为1，c为选自0.847～1.712中的任意数值，c优选为1.279。当受试者的抗缪勒氏管激素(AMH)水平在2ng/ml及以上时，AMH为0；当受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml时，AMH为1，d为选自2.708～3.701中的任意数值，d优选为3.204，当受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml时，AMH为1，d为选自1.985～2.887中的任意数值，d优选为2.436，当受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml时，AMH为1，d为选自1.153～2.070中的任意数值，d优选为1.612，当受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml时，AMH为1，d为选自0.230～1.356中的任意数值，d优选为0.793。
随后利用2018年的3273名受试者的数据利用上述分组依据和公式对数据进行验证。通过如上验证，确认了如上所述构建的模型的获得可以良好地预测受试者的卵巢储备功能。
为了验证系统的准确性，我们利用JMP Pro 14.2软件的评估功能评估了本申请系统和在先申请系统(CN201811516206.4)针对相同人群进行评估的准确性，结果参见下表4，从该结果可以看出，本实施例构建的系统和在先申请的系统可以达到相同的评估水平。
表4
Figure PCTCN2020102088-appb-000016
Figure PCTCN2020102088-appb-000017
由此，根据上述公式一可以基于对某一受试者的年龄、月经周期任一天抗缪勒氏管激素浓度，以及月经2-4天的静脉血中的卵泡刺激素浓度来计算这个受试者的卵巢低反应概率。
如上所述，本发明的发明人首次应用月经周期任一天的血清AMH水平、年龄及月经2-4天的血清FSH水平三个指标的对卵巢储备功能进行评估。与之前的系统相比，可以不再使用经过阴道超声学探查的方法计数的窦卵泡计数(AFC)，但其准确性依然可以达到之前系统的水平,此外，由于不需要检测窦卵泡计数(AFC)，可以降低检测成本低。另外，影响AFC检测结果的因素较多，与AFC相比，FSH和AMH结果的准确性和可重复性更好，利用本申请的三指标系统可以打到与四指标系统，相类似的效果。
利用本发明的系统和方法可以快速并准确地评估受试者的卵巢储备水平，解决了现有技术中主要根据医生经验和一些简单的根据卵巢储备指标切点值来进行评估卵巢储备功能所带来的可重复性差，标准不统一的问题。
基于当前卵巢储备预测达到某种卵巢储备状态的年限(年龄)
选取2017年1月至2018年12月在北京大学第三医院生殖医学中心接受体外受精/胞浆内精子注射-胚胎移植(IVF/ICSI-ET)新鲜周期的患者，共16280名受试者，其中受试者的入选标准如下：1)年龄在20至50岁之间的女性；2)所有激素水平均在北京大学第三医院生殖医学中心的内分泌实验室进行了测试。
在本实施例中，发明人收集了受试者的病历，获取了包括年龄、体重指 数(BMI)、不孕持续时间、以前的IVF/ICSI-ET尝试次数、血清基础雌二醇(E 2)、基础黄体生成激素(LH)，卵泡刺激素(FSH)水平和抗缪勒氏管激素(AMH)水平，左右卵巢的窦卵泡计数(AFC)等数据，以及记录不孕原因等。
获取样品和内分泌测定
在月经周期的第二天，收集上述受试者(共16280名)的静脉血进行FSH，LH和E2检查。使用西门子Immulite 2000免疫测定系统(Siemens Healthcare Diagnostics,Shanghai,P.R.China)，通过Bio-RAD实验室的质量控制(Lyphochek Immunoassay Plus Control,Trilevel,catalog number 370,lot number 40340)来检测所有受试者的血清中的FSH，LH和E 2。在月经周期的任意一天收集用于AMH检查的血液，并通过试剂盒中的具有质量控制的超灵敏两点ELISA(Ansh Labs，USA)测量血清AMH水平。在月经周期第2天，由经验丰富的技术人员通过经阴道超声扫描确定左右卵巢中的AFC(直径2-10mm的卵泡)。
在本实施例中，判断受试者为DOR的标准为按照之前的卵巢储备评估模型计算的预测卵巢低反应概率大于等于50％，将受试者是否DOR作为模型构建的结果变量。
利用上述本申请计算卵巢低反应模型(公式一)计算的受试者当前的卵巢低反应概率p 0和变量的分类方式，按照预测的当前的卵巢低反应概率p 0，可以将人群的卵巢储备分成4组，其中，D组人群为预测的DOR人群，如下表5所示。基于表5，本申请的发明人将DOR人群定义为女性，其卵巢低反应预测概率超过50％，即p 0大于等于0.5。在本实施例中，基于此模型能够将所有受试者分为非DOR组和DOR组。
本申请的发明人基于已发表的文章(Xu et al.Journal of assisted reproduction and genetic.2020.37:963-972)，即利用之前四参数卵巢储备评估模型得到的不同卵巢储备情况与生育力的关系，以寻找卵巢储备变化导致生育力变化的特定卵巢储备情况(即卵巢低反应概率)，结果如下表5所示。表5中，利用聚类分析按照预测低反应概率(或称预测DOR概率)对人群进行分类，一共分成四类，进而对每组人的启动周期或者胚胎移植周期的实际临床 妊娠率和活产率进行统计分析，以显示各组生育力的变化。基于表5可以看出，如果受试者的卵巢储备低反应概率大于等于50％，即D组人群的临床妊娠率和活产率均下降，如果受试者的卵巢储备低反应概率大于等于25％，即C组人群的启动周期临床妊娠率也出现下降，说明生育力下降开始。
表5.四个卵巢储备组的临床妊娠率和活产率。
Figure PCTCN2020102088-appb-000018
ET：胚胎移植
基于表5，将人群分成四组，即A组、B组、C组和D组。表5显示了将这些受试者分成4组之后，每个组的临床妊娠率和活产率。其中D组的DOR人群无论是每起始周期临床妊娠率和活产率还是每移植周期临床妊娠率和活产率均大于A组和B组人群，提示生育力下降明显。因此提示人群在进入DOR之前就应该尽早尝试生育。表5中的数据也显示C组启动周期的临床妊娠率也出现下降，提示p＝25％时已经开始了生育力的早期下降。
因此，如上所述利用本申请涉及的变量分类方式将人群分4类，如果该类人群的pDOR大于等于0.5，则在该人群中出现DOR的概率非常大。同时如果pDOR大于等于0.25，则认定在该类人群中会开始出现DOR，如果pDOR接近1，则判断该类人群的卵巢储备功能基本耗尽。因此，可以基于pDOR将人群分成三大组，即pDOR小于0.25组，pDOR大于等于0.25小于0.5组，以及pDOR大于等于0.5小于1组。
进一步，为了获取受试者从目前的pDOR概率进展到pDOR＝0.25，pDOR＝0.5或pDOR＝1时分别需要多少年。我们利用上述16820名受试者的当前的卵巢储备情况，根据各年龄段DOR的比例建立了DOR比例与年龄关系的逻辑曲线图(生长曲线)，如图1所示。
图1逻辑曲线(生长曲线)结果展示了卵巢储备情况(pDOR)随年龄 变化的趋势。
如图1所示，图1的曲线符合如下公式：
Figure PCTCN2020102088-appb-000019
其中，利用逻辑曲线参数拟合的数据如下表2所示。
表6
参数 预估值 标准偏差 WaldChiSquare Prob>ChiSquare Lower95％ Upper95％
生长速率(x) 0.226 0.012 333.26192 <.0001* 0.202 0.250
拐点值(y) 43.240 0.241 32241.875 <.0001* 42.768 43.712
其中，公式二中，x表示生长速率，基于表6可以看出，x为选自0.202-0.250中的任意数值，优选x为0.226，y表示拐点值，基于表6可以看出，y为选自42.768～43.712中的任意数值，优选y为43.240。
为了进一步计算某一受试者发展到卵巢储备下降导致生育力明显下降，即低反应概率达到50％的时间，可以采用下述公式，即公式三来进行计算。
Figure PCTCN2020102088-appb-000020
其中，age2表示受试者出现卵巢储备明显下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
同理，如果希望计算某一受试者发展到卵巢储备功能基本耗尽，即低反应概率达到95％的时间，可以采用下述公式，即公式四来进行计算。
Figure PCTCN2020102088-appb-000021
其中，age3表示受试者卵巢储备耗尽的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
同理，如果希望计算某一受试者发展到卵巢储备功能下降导致生育力早期下降，即低反应概率达到25％的时间，可以采用下述公式，即公式五来进行计算。
Figure PCTCN2020102088-appb-000022
其中，age4表示受试者卵巢储备开始下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
如上所述，利用上述公式一预测的p实际上是预测的受试者当前的低反应发生率p 0，预测的低反应或称DOR人群定义为预测概率大于等于50％，因此计算的是进展到pDOR＝0.5时需要多少年，将4796名受试者利用公式一计算DOR概率带入公式三，得到不同人群的预测到DOR的时间(predicted time-to-DOR，TTD)如下表7所示：
表7.结果展示举例，基于公式一计算的DOR概率计算的受试者从目前的卵巢储备情况发展到卵巢储备下降到导致生育力开始下降(低反应概率25％)，卵巢储备下降导致生育力明显下降(低反应概率50％)，以及卵巢储备接近耗竭导致生育力接近耗竭(低反应概率95％)所需的时间。
Figure PCTCN2020102088-appb-000023
可以看出，利用本申请的公式一和公式三，可以预测受试者发展到生育力明显下降的年限，计算这个参数对于受试者意义显著，可以提示受试者应该尽早地计划适合自己的生育年龄。如表7所示，对于第一组的人群，预测其出现生育力出现开始下降的年限是10.1年，出现生育力显著下降的年限是14.5年，生育生育力接近耗竭的年限是26.2年。对于其他各组人群，也可以利用相同的方法有效地预测。
如上所述，利用公式四和公式五也可以分别计算受试者从当前的卵巢低反应概率到受试者的卵巢储备下降到导致生育力开始下降，即卵巢低反应概率(p)升高到25％所需的年限，以及受试者从当前的卵巢低反应概率到受试者的卵巢储备接近耗竭导致生育力接近耗竭，即卵巢低反应概率(p)升高到95％所需的年限，从而对女性合理安排生育计划以及围绝经期健康管理具有重要意义，这可能是降低育龄妇女不育率的有效方法，同时也有利于女性的围绝 经期健康管理。
尽管以上对本发明的实施方案进行了描述，但本发明并不局限于上述的具体实施方案和应用领域，上述的具体实施方案仅仅是示意性的、指导性的，而不是限制性的。本领域的普通技术人员在本说明书的启示下和在不脱离本发明权利要求所保护的范围的情况下，还可以做出很多种的形式，这些均属于本发明保护之列。

Claims (13)

  1. 一种用于预测受试者出现卵巢储备新变化年限或年龄的系统，其包括：
    数据采集模块，其用于获取受试者的年龄、抗缪勒氏管激素(AMH)水平、卵泡刺激素(FSH)水平的数据；
    计算卵巢储备功能的模块，其利用将受试者年龄、受试者抗缪勒氏管激素(AMH)水平、受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量来计算受试者的当前卵巢低反应概率(p 0)，即当前的卵巢储备情况；
    计算受试者出现卵巢储备下降到某程度年限的模块，其利用受试者当前的卵巢储备即卵巢低反应概率(p 0)来计算受试者出现卵巢储备新变化的年限或年龄。
  2. 根据权利要求1所述的系统，其中，所述抗缪勒氏管激素(AMH)水平是指女性受试者月经周期任意一天的静脉血中的抗缪勒氏管激素浓度，所述卵泡刺激素(FSH)水平是指女性受试者月经第2天的静脉血中的卵泡刺激素浓度。
  3. 根据权利要求1或2所述的系统，其中，
    受试者出现卵巢储备下降到某程度是指以下三种情况：
    受试者的卵巢储备下降到导致生育力开始下降，即卵巢低反应概率(p)升高到25％；
    受试者的卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率(p)升高到50％；
    受试者的卵巢储备接近耗竭导致生育力接近耗竭，即卵巢低反应概率(p)升高到95％。
  4. 根据权利要求3所述的系统，其中，
    预测受试者的卵巢储备下降到导致生育力开始下降的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于25％所 需的年限；
    预测受试者的卵巢储备明显下降导致生育力明显下降的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于50％所需的年限；
    预测受试者的卵巢储备接近耗竭导致生育力耗竭的年限是指：利用计算卵巢储备功能的模块计算受试者的当前卵巢储备，即当前的卵巢低反应概率(p 0)，然后计算达到目标卵巢储备情况，即卵巢低反应概率(p)等于95％所需的年限。
  5. 根据权利要求1所述的系统，其中，
    在计算卵巢储备功能的模块中，将受试者年龄转换成三分类变量，
    即将受试者的年龄分为三组，分别为：受试者的年龄在30岁及以下，受试者的年龄在大于30岁且在40岁及以下，以及受试者的年龄大于40岁。
  6. 根据权利要求1述的系统，其中，
    在计算卵巢储备功能的模块中，将受试者的泡刺激素(FSH)水平转换成四分类变量，
    即将受试者的泡刺激素(FSH)水平分为四组，分别为：受试者的泡刺激素(FSH)水平小于6.5IU/L，受试者的泡刺激素(FSH)水平在6.5及IU/L以上且小于8.5IU/L，受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L，以及受试者的泡刺激素(FSH)水平在10.5IU/L及以上。
  7. 根据权利要求1所述的系统，其中，
    在计算卵巢储备功能的模块中，将受试者的抗缪勒氏管激素(AMH)水平转换成五分类变量，
    即将受试者的抗缪勒氏管激素(AMH)水平分为五组，分别为：受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml，受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml，受试者的抗缪勒氏管激素(AMH)水平 在1.5ng/ml及以上且小于2ng/ml，以及受试者的抗缪勒氏管激素(AMH)水平大于等于2ng/ml。
  8. 根据权利要求1～7中任一项所述的系统，其中，
    在计算卵巢储备功能的模块中，预先存储有基于现有数据库中受试者的受试者年龄、受试者抗缪勒氏管激素(AMH)水平以及受试者卵泡刺激素(FSH)水平的数据转换成的多分类变量拟合而成的用于预测受试者当前的卵巢低反应概率(p 0)的公式。
  9. 根据权利要求8所述的系统，其中，
    所述公式为如下公式一：
    Figure PCTCN2020102088-appb-100001
    其中，p 0为计算出的用于表征所述受试者当前的卵巢储备功能的参数，
    其中，a、b、c和d为无单位参数；
    其中，在计算卵巢储备功能的模块中，基于受试者的年龄、受试者的抗缪勒氏管激素(AMH)水平和受试者的泡刺激素(FSH)水平来获取b、c和d的取值来带入公式一进行计算，在计算中age，FSH，以及AMH取值为0或1。
  10. 根据权利要求9所述的系统，其中，
    a为选自-4.072～-3.188中的任意数值，a优选为-3.630；
    当受试者的年龄在30岁及以下时，age为0，
    当受试者的年龄在大于30岁且在40岁及以下，age为1，b为选自0.163～0.960中的任意数值，b优选为0.561，以及
    当受试者的年龄大于40岁时，age为1，b为选自0.295～1.317中的任意数值，b优选为0.806；
    当受试者的泡刺激素(FSH)水平小于6.5IU/L时，FSH为0，
    当受试者的泡刺激素(FSH)水平在6.5IU/L及以上且小于8.5IU/L时，FSH为1，c为选自0.239～1.006中的任意数值，c优选为0.622，
    当受试者的泡刺激素(FSH)水平在8.5IU/L及以上且小于10.5IU/L时，FSH为1，c为选自0.363～1.303中的任意数值，c优选为0.833，以及
    当受试者的泡刺激素(FSH)水平在10.5IU/L及以上时，FSH为1，c为选自0.847～1.712中的任意数值，c优选为1.279；
    当受试者的抗缪勒氏管激素(AMH)水平在2ng/ml及以上时，AMH为0；
    当受试者的抗缪勒氏管激素(AMH)水平小于0.5ng/ml时，AMH为1，d为选自2.708～3.701中的任意数值，d优选为3.204，
    当受试者的抗缪勒氏管激素(AMH)水平在0.5ng/ml及以上且小于1ng/ml时，AMH为1，d为选自1.985～2.887中的任意数值，d优选为2.436，
    当受试者的抗缪勒氏管激素(AMH)水平在1ng/ml及以上且小于1.5ng/ml时，AMH为1，d为选自1.153～2.070中的任意数值，d优选为1.612，
    当受试者的抗缪勒氏管激素(AMH)水平在1.5ng/ml及以上且小于2ng/ml时，AMH为1，d为选自0.230～1.356中的任意数值，d优选为0.793。
  11. 根据权利要求8-10任一项所述的系统，其中，
    在计算受试者出现卵巢储备下降到某程度年限的模块中，利用如下公式三来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备明显下降导致生育力明显下降，即卵巢低反应概率为50％的年限：
    Figure PCTCN2020102088-appb-100002
    其中，age2表示受试者出现卵巢储备明显下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
  12. 根据权利要求8-10任一项所述的系统，其中，
    在计算受试者出现卵巢储备下降到某程度年限的模块中，利用如下公式四来计算受试者从当前的卵巢储备情况(p 0)到出现卵巢储备接近耗尽(生育力接近耗竭)，即卵巢低反应概率为95％的年限：
    Figure PCTCN2020102088-appb-100003
    其中，age3表示受试者卵巢储备接近耗竭的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
  13. 根据权利要求8-10任一项所述的系统，其中，
    在计算受试者出现卵巢储备下降到某程度年限的模块中，利用如下公式五来计算受试者从当前的卵巢储备情况(p 0)到卵巢储备下降导致生育力开始下降，即卵巢低反应概率为25％的年限：
    Figure PCTCN2020102088-appb-100004
    其中，age4表示受试者卵巢储备开始下降的年龄，age1表示受试者的当前年龄，其中x为选自0.202-0.250中的任意数值，优选x为0.226。
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109602394A (zh) * 2018-12-12 2019-04-12 北京大学第三医院 评估受试者卵巢储备功能的系统

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2370502A1 (en) * 1999-04-26 2000-11-02 John L. Hammons Feminine sanitary disposable article having a blood detection means as sensor
CN105224827B (zh) * 2008-07-01 2019-12-24 小利兰·斯坦福大学托管委员会 用于发展ivf治疗协议的分析数据的计算机系统和存储介质
EP2687851A1 (en) * 2012-07-20 2014-01-22 Matricelab Innove Method for increasing implantation success in assisted fertilization
TWI778979B (zh) * 2016-09-30 2022-10-01 瑞士商麥歐文科學有限公司 治療女性不孕症之方法
WO2018177746A1 (en) * 2017-03-31 2018-10-04 Institut National De La Sante Et De La Recherche Medicale (Inserm) A gnrh antagonist for use in the treatment of a women affected with polycystic ovary syndrome
WO2018200820A1 (en) * 2017-04-26 2018-11-01 Celmatix Inc. A measurement system and method for defining and determining an ovarian reserve
CN109781763A (zh) * 2018-11-26 2019-05-21 首都医科大学附属北京妇产医院 一种卵母细胞质量评价方法
CN109781762A (zh) * 2018-11-26 2019-05-21 首都医科大学附属北京妇产医院 一种筛选卵巢储备功能低下代谢标志物的方法
CN110916778A (zh) * 2019-10-11 2020-03-27 上海交通大学医学院附属瑞金医院 经阴道小卵泡穿刺术改善多囊卵巢患者卵巢低反应的方法

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109602394A (zh) * 2018-12-12 2019-04-12 北京大学第三医院 评估受试者卵巢储备功能的系统

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
HOU RONGRONG ·, LI LI: "The Application of Amh in the Determination of Ovarian Reserve Function in the Perimenopausal Women and the Prediction of the Age of Menopause", CHINA MODERN DOCTOR, vol. 54, no. 14, 31 May 2016 (2016-05-31), pages 165 - 168, XP055885915, ISSN: 1673-9701 *
LIU YANFEI, SHI XINYAN, LU HONGMEI, SHEN YONGHAI, CHEN YU: "Anti-Mullerian Hormone (AMH) and Advances in Its Application in Predicting Menopause", CHINESE JOURNAL OF HEALTH LABORATORY TECHNOLOGY, ZHONG GUO WEI SHENG JIAN YAN ZA ZHI BIAN JI BU, CN, vol. 26, no. 15, 30 August 2016 (2016-08-30), CN , pages 2278 - 2280, XP055885912, ISSN: 1004-8685 *
TEHRANI FAHIMEH RAMEZANI, SOLAYMANI-DODARAN MASOUD, TOHIDI MARYAM, GOHARI MAHMOOD REZA, AZIZI FEREIDOUN: "Modeling Age at Menopause Using Serum Concentration of Anti-Mullerian Hormone", JOURNAL OF CLINICAL ENDOCRINOLOGY AND METABOLISM, THE ENDOCRINE SOCIETY, US, vol. 98, no. 2, 1 February 2013 (2013-02-01), US , pages 729 - 735, XP055885908, ISSN: 0021-972X, DOI: 10.1210/jc.2012-3176 *
WEI JINGRUI, YUAN JINHUI BAI JINGYING ZHU JIANI TAN LI: "Comparison Value of Common Indicators for Assessing Ovarian Reserve Function in Predicting Ovarian Response", JOURNAL OF REPRODUCTIVE MEDICINE, XX, CN, vol. 29, no. 1, 30 January 2020 (2020-01-30), CN , pages 27 - 32, XP055856772, ISSN: 1004-3845, DOI: 10.3969/j.issn.1004-3845.2020.01.006 *

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