WO2022006942A1 - 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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WO2022006942A1
WO2022006942A1 PCT/CN2020/102090 CN2020102090W WO2022006942A1 WO 2022006942 A1 WO2022006942 A1 WO 2022006942A1 CN 2020102090 W CN2020102090 W CN 2020102090W WO 2022006942 A1 WO2022006942 A1 WO 2022006942A1
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ovarian
ovarian reserve
age
years
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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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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

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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 function, which is used to calculate the above-mentioned information obtained in the data acquisition module, thereby calculating the subject. Probability (p) of ovarian hyporesponsiveness in patients.
  • 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 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 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, In order to judge the level of its current ovarian reserve function, and evaluate the level of ovarian reserve.
  • this application involves the following:
  • a system for calculating the 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 the data of the subject's age, anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, and antral follicle count (AFC);
  • a module for calculating ovarian reserve which utilizes a receiver operating characteristic (ROC) curve to detect the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) acquired in the data acquisition module age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, antral follicle count (AFC) Converting into a binary variable, thereby using the binary variable as a predictor variable to calculate the subject's current probability of low ovarian response (p 0 ), that is, the current ovarian reserve;
  • ROC receiver operating characteristic
  • the module for calculating the number of years in which the subject's ovarian reserve has declined to a certain degree uses 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 follicle-stimulating hormone (FSH) level refers to the female subject Follicle-stimulating hormone concentration in venous blood on day 2 of menstruation
  • the antral follicle count (AFC) is a vaginal ultrasound count of all visible follicles 2-10 mm in diameter in both ovaries of female subjects on day 2 of menstruation number of.
  • 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 cut-point value of the age was 35 years
  • the cut-point value of the anti-Mullerian hormone (AMH) level was 1.2 ng/ml
  • the cut-point value of the follicle stimulating hormone (FSH) level was 8 IU/L
  • the cut-point value of the Antral Follicle Count (AFC) was 8.
  • the subject's age, subject's anti-Mullerian hormone (AMH) level, and subject's follicle-stimulating hormone (FSH) level based on the existing database are stored in advance.
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063 to 1.342
  • b is any numerical value selected from -2.542 ⁇ -1.056
  • c is any numerical value selected from 0.548 ⁇ 1.838
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 0.219-0.265, preferably x is 0.242.
  • a method for calculating the number of years or age at which a new change in ovarian reserve occurs in a subject comprising:
  • AMH anti-Mullerian hormone
  • FSH follicle-stimulating hormone
  • AFC antral follicle count
  • Ovarian reserve is calculated using receiver operating characteristic (ROC) curves to detect subject age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, The cut-off point for antral follicle count (AFC), and age, anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, and antral follicle count (AFC) were converted into A dichotomous variable, thereby using the dichotomous variable as a predictor variable to calculate the subject's current probability of low ovarian response (p 0 ), that is, the current ovarian reserve;
  • ROC receiver operating characteristic
  • 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 follicle-stimulating hormone (FSH) level refers to the female subject Follicle-stimulating hormone concentration in venous blood on day 2 of menstruation
  • the antral follicle count (AFC) is a vaginal ultrasound count of all visible follicles 2-10 mm in diameter in both ovaries of female subjects on day 2 of menstruation number of.
  • 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 cut-point value of the age was 35 years
  • the cut-point value of the anti-Mullerian hormone (AMH) level was 1.2 ng/ml
  • the cut-point value of the follicle stimulating hormone (FSH) level was 8 IU/L
  • the cut-point value of the Antral Follicle Count (AFC) was 8.
  • step of calculating ovarian reserve using the subject's age, subject's anti-Mullerian hormone (AMH) level, subject's follicle-stimulating hormone (FSH) level, subject's A formula for predicting the subject's current probability of low ovarian response (p 0 ) was calculated by fitting the dichotomous variables into the data of the subject's antral follicle count (AFC).
  • AFC antral follicle count
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063 to 1.342
  • b is any numerical value selected from -2.542 ⁇ -1.056
  • c is any numerical value selected from 0.548 ⁇ 1.838
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 invention can help subjects to calculate the years when their ovarian reserve has undergone key changes, and prompt the population to try fertility as soon as possible before entering 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 female fertility. 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 subject is predicted to have a low response, and the subject is diagnosed as DOR.
  • 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 mechanism of the decline of ovarian reserve with age is as follows: (1) The number of follicles decreases, primordial follicles appear after the sex differentiation of the embryo, and the number of follicles is the largest at this time. After puberty, the follicles begin to develop and mature. The undischarged follicles 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. (2) Decreased egg quality.
  • Embryonic quality is mainly determined by egg quality. Older age can lead to increased risk of egg aneuploidy, increased risk of mitochondrial dysfunction, loss of egg polarity, and egg cell epigenetic changes. (3) 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 regulate the secretion of pituitary FSH, and a decrease in the level of inhibin B leads to an increase in the secretion of FSH in the luteal phase.
  • the early increase in FSH promotes the growth of new follicles and E2 secretion, which ultimately shortens the menstrual cycle.
  • Serum FSH levels increased, inhibin B levels decreased, and follicle sensitivity to FSH decreased, suggesting a decrease in the number of antral follicles that could be recruited.
  • 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 calculating the years or age of new changes in ovarian reserve in a subject, comprising: a data acquisition module for acquiring the subject's age, anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC) data; a module for calculating ovarian reserve, which uses receiver operating characteristic (ROC) curves to detect subject age, The cut-off point of anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC), and age, anti-Mullerian hormone (AMH) level according to the cut-point value of the cut-off point ) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC) were converted into dichotomous variables, thereby using the dichotomous variables as predictors to calculate the subject's current probability of low ovarian response
  • Current ovarian reserve a module for calculating the number of years when the subject's ovarian reserve has declined to a certain degree, which uses the subject's current ovarian reserve, namely the probability of low ovarian response (p 0 ), to calculate the subject's new change in ovarian reserve. age or age.
  • the present application relates to a method for calculating the number of years 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 hormone (FSH) levels, antral follicle count (AFC) data; calculation of ovarian reserve using receiver operating characteristic (ROC) curves to detect subject age, anti-Mullerian Cut-off point of tube hormone (AMH) level, follicle stimulating hormone (FSH) level, antral follicle count (AFC), and age, anti-Mullerian hormone (AMH) level, follicle count (AFC) according to the cut-point value of the cut-off point Stimulating hormone (FSH) levels, antral follicle counts (AFC) were converted into dichotomous variables to use the dichotomous variables as predictors to calculate the subject's current probability of low ovarian response (p 0 ), i.e
  • 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, follicle-stimulating hormone (AMH), in the venous blood of a female subject on any day of the menstrual cycle.
  • FSH follicle-stimulating hormone
  • AFC tral follicle count
  • anti-Mullerian hormone is a hormone secreted by the granulosa cells of the ovarian follicles.
  • the female baby in the fetal period begins to produce AMH from the age of 9 months, and the number of small follicles in the ovary increases.
  • the higher the concentration of AMH the higher the concentration of AMH; on the contrary, when the follicles are gradually consumed with age and various factors, the concentration of AMH will also decrease accordingly.
  • 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.
  • Antral follicle count refers to the number of all visible follicles 2-10 mm in diameter in both ovaries on day 2 of menstruation. AFC can measure and count follicles by ultrasound.
  • the occurrence of a decline in ovarian reserve in a subject refers to the following three situations: the decline in the ovarian reserve of the subject leads to a decline in fertility, that is, the probability of low ovarian response (p ) increased to 25%; the subject’s ovarian reserve was significantly decreased resulting in a significantly decreased fertility, i.e. the probability of low ovarian response (p) was increased to 50%; the subject’s ovarian reserve was nearly depleted resulting in a near-depleted fertility, i.e. The probability of low ovarian response (p) increased to 95%.
  • predicting that the ovarian reserve of the subject declines to the number of years leading to the decline of fertility refers to: calculating the current ovarian reserve of the subject by using the module for calculating the ovarian reserve function, that is, the current ovarian reserve.
  • the number of years required to predict the ovarian reserve of the subject is close to depletion and the depletion of fertility refers to: using the module for calculating the ovarian reserve function to calculate the current ovarian reserve of the subject, that is, the current probability of low ovarian response (p 0 ), The number of years required to achieve the target ovarian reserve, ie the probability of low ovarian response (p) equal to 95%
  • the ovarian reserve function which uses the receiver operating characteristic (ROC) curve to detect the age of the subject obtained in the data acquisition step, the The cutoff point of Lehrmanian hormone (AMH) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC), and age, anti-Mullerian hormone (AMH) level according to the cutpoint value of the cutoff point , follicle-stimulating hormone (FSH) levels, and antral follicle count (AFC) were converted into dichotomous variables to use the dichotomous variables as predictors to calculate the subject's current probability of low ovarian response (p 0 ), specifically , the cut-point value for the above age is 35 years, the cut-point value for the anti-Mullerian hormone (AMH) level is 1.2ng/ml, the cut-point value for the follicle-stimulating hormone (FSH) level
  • ROC receiver operating characteristic
  • the module or step of calculating ovarian reserve using the subject's age, subject's anti-Mullerian hormone (AMH) level, subject's follicle stimulating hormone (FSH) level based on the existing database .
  • the data of the subject's antral follicle count (AFC) is converted into a formula for predicting the current probability of low ovarian response (p 0 ) of the subject, which is fitted to a binary variable to calculate.
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063 to 1.342
  • b is any numerical value selected from -2.542 ⁇ -1.056
  • c is any numerical value selected from 0.548 ⁇ 1.838
  • 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 data of the subject's age, anti-Mullerian hormone (AMH) level, follicle stimulating hormone (FSH) level, antral follicle count (AFC), and then using the subject's data Age, anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC) cut-off points for the subject's age, anti-Mullerian hormone (AMH) level, Data on follicle-stimulating hormone (FSH) levels and antral follicle count (AFC) were transformed into dichotomous variables.
  • the value of the age parameter brought into formula 1 after its binary variable is 0. If the age value is greater than or equal to 35 years old, then its binary classification variable After the variable, the value of the age parameter brought into formula 1 is 1; if the anti-Mullerian hormone (AMH) level of a subject is lower than 1.2ng/ml, then after the binary variable, the value of the age parameter is brought into The value of the AMH parameter in formula 1 is 1. If the anti-Mullerian hormone (AMH) level of a subject is greater than or equal to 1.2ng/ml, the AMH in formula 1 will be brought into the second category after the variable. The value of the parameter is 0.
  • the value of the FSH parameter brought into formula 1 is 1 after the binary variable.
  • a subject's follicle-stimulating hormone (FSH) level is lower than 8IU/L, then after its binary variable, the FSH parameter brought into formula 1 takes the value of 1.
  • FSH follicle-stimulating hormone
  • the value of the FSH parameter brought into formula 1 is 0.
  • the value of the AFC parameter brought into formula 1 is 1. If a subject's antral follicle count (AFC) level is greater than or equal to 8, then after the binary variable, the AFC in formula 1 is brought into The value of the parameter is 0.
  • anti-Mullerian hormone (AMH) level Based on the subject's age, anti-Mullerian hormone (AMH) level, follicle-stimulating hormone (FSH) level, antral follicle count (AFC) obtained in the above formula 1 and the data acquisition module or step, and according to the cutoff point Cut point values to convert age, anti-Mullerian hormone (AMH) levels, follicle-stimulating hormone (FSH) levels, antral follicle count (AFC) into dichotomous variables to calculate using the dichotomous variables as predictors Subject's current probability of ovarian hyporesponsiveness (p 0 ).
  • AMD anti-Mullerian hormone
  • FSH follicle-stimulating hormone
  • AFC tral follicle count
  • the subject is determined to be DOR, that is, the subject is predicted to be a low ovarian responder.
  • the time required for the subject's ovarian reserve to reach a certain change in ovarian reserve in the future can be calculated.
  • the inventor of this application is based on his published article (Xu et al. Journal of assisted reproduction and genetic. 2020.37:963–972), that is, the different ovarian reserve conditions obtained by using the previous four-parameter ovarian reserve assessment model In relation to fertility, look for specific ovarian reserve profiles (ie, probability of low ovarian response) where changes in ovarian reserve lead to changes in fertility. That is, using cluster analysis to classify the population according to the predicted low response probability, there are four categories in total.
  • the inventors of the present application assessed the current ovarian reserve of 16,820 subjects according to the previous ovarian reserve assessment model, and established a logistic curve (growth curve) of the relationship between the DOR ratio and age according to the DOR ratio in each age group, As shown in Figure 1.
  • age is an important factor in the occurrence of DOR.
  • Logistic regression analysis showed that people with increasing age had a higher risk of developing 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 0.219-0.265, preferably x is 0.242.
  • the results of age2-age1 give the years from the current age to the time when the subject has a significant decline in ovarian reserve and a significant decline in fertility.
  • 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 0.219-0.265, preferably x is 0.242.
  • the age3-age1 results give the number of years that progress from the current age to the near-depletion of the subject's ovarian reserve leading to near-depletion of fertility.
  • 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 0.219-0.265, preferably x is 0.242.
  • the age4-age1 results give the number of years from the development of the current age to when the subject's ovarian reserve begins to decline leading to the onset of a decline in fertility.
  • 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.
  • 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
  • SAS JMP Pro software version 14.2
  • age, BMI cause of infertility
  • number of AFC counted by vaginal ultrasound on the second day of menstruation
  • FSH level on the second day of menstruation
  • the associations of AMH levels on any day of menstruation, LH levels on menstruation 2 days, and E2 levels on 2 menstrual days and ovarian hyporesponsiveness as outcome variables were analyzed.
  • the set outcome variable was low ovarian response, it was confirmed that age, AMH level, FSH level and number of AFC were significantly associated with ovarian reserve (all P values less than 0.05 were considered to have Statistical significance).
  • 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 cut-off point (cut-off point) of four continuous variables related to low ovarian response was determined by using the ROC (receiver operating characteristic) curve method, and according to the cut-off point (cut-off point), the variable is changed, and the continuous variable is turned into a binary variable.
  • Age, AMH, FSH, and AFC were converted into dichotomous variables by using the cutpoint value of the ROC curve.
  • the ROC curve was used to determine the cut-off points of age, AMH, FSH, and AFC, and the tangent values of the cut-off points were determined respectively.
  • the results found that the cut-point values of age, AMH, FSH, and AFC were 35, 1.2, 8, and 8, respectively. It can be confirmed that the results of the four indicators are as follows: age, the cut-point values of AMH, FSH and AFC are 35 years old, 1.2ng/ml, 8IU/L and 8 respectively, thus the age is divided into ⁇ 35 and >35, Age, AMH, FSH, and AFC were converted into dichotomous variables according to the above criteria.
  • the specific embodiment adopts the number of AFC counted by vaginal B-ultrasound on the second day of menstruation, FSH levels on day 2 of menstruation, AMH levels on any day of menstruation, and, as described above, based on ROC curves for 16,820 subjects, adjusted for cut-off points for age, AMH, FSH, and AFC converted to dichotomous variables value.
  • the probability p of the subject's ovarian low response calculated above is the current probability of low ovarian response (p 0 ) of the subject, which is the same as the above formula and is used to predict the subject
  • the formula for the current probability of low ovarian response (p 0 ) is the following formula 1:
  • p 0 is the calculated parameter used to characterize the current ovarian reserve function of the subject
  • i is any value selected from -1.786 to -0.499
  • a is any value selected from 0.063 to 1.342
  • b is any numerical value selected from -2.542 ⁇ -1.056
  • c is any numerical value selected from 0.548 ⁇ 1.838
  • the D group population is the predicted DOR population, as shown in Table 1 below.
  • the inventors of the present application defined the DOR population as females whose predicted probability of low ovarian response exceeds 50%, ie, p 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 1 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.
  • Figure 1 Logistic curve (growth curve) results showing the trend of ovarian reserve (pDOR) with age.
  • x represents the growth rate. Based on Table 2, it can be seen that x is any value selected from 0.219-0.265, preferably x is 0.242, and y represents the inflection point value. Based on Table 2, it can be seen that y is the selected value. Any value from 40.905 to 41.733, preferably y is 41.319.
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 0.219-0.265, preferably x is 0.242.
  • 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 0.219-0.265, preferably x is 0.242.
  • 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

用于计算受试者出现卵巢储备新变化年限的系统，其包括：数据采集模块，用于获取受试者的年龄、抗缪勒氏管激素水平、卵泡刺激素水平、窦卵泡计数的数据；计算卵巢储备功能的模块，利用受试者工作特征曲线来检测数据采集模块中获取的受试者年龄、抗缪勒氏管激素水平、卵泡刺激素水平、窦卵泡计数的分界点，并根据该分界点的切点值来将年龄、抗缪勒氏管激素水平、卵泡刺激素水平、窦卵泡计数转换成二分类变量，从而利用二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)；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的数量差异大，出生时从数万至数百万不等。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
在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)，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，从而判断其当前卵巢储备功能所处的水平，并对卵巢储备水平进行评估。
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(随着年龄增长的累积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。
具体来说，本申请涉及如下内容：
1.一种用于计算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、窦卵泡计数(AFC)的数据；
计算卵巢储备功能的模块，其利用受试者工作特征(ROC)曲线来检测所述数据采集模块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、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.根据项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，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经第2天时的两个卵巢中直径为2-10mm的所有可见卵泡的个数。
3.根据项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.根据项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.根据项1～4中任一项所述的系统，其中，
所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为1.2ng/ml，所述卵泡刺激素(FSH)水平的切点值为8IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
6.根据项5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、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二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。
7.根据项6所述的系统，其中，
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 PCTCN2020102090-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，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
8.根据项7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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
9.根据项7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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
10.根据项7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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
11.一种用于计算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、窦卵泡计数(AFC)的数据；
计算卵巢储备功能，其利用受试者工作特征(ROC)曲线来检测所述数据采集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、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。
12.根据项11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，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经第2天时的两个卵巢中直径为2-10mm的所有可见卵泡的个数。
13.根据项11或12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95％。
14.根据项13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。
15.根据项11～14中任一项所述的方法，其中，
所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为1.2ng/ml，所述卵泡刺激素(FSH)水平的切点值为8IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
16.根据项15所述的方法，其中，
在计算卵巢储备功能的步骤中，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、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二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。
17.根据项16所述的方法，其中，
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 PCTCN2020102090-appb-000005
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，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
18.根据项17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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
19.根据项17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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
20.根据项17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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
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。利用本发明的系统和方法，可以帮助受试者计算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，利用本发明的系统和方法，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，是女性生育力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)。
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。
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％。
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计算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、窦卵泡计数(AFC)的数据；计算卵巢储备功能的模块，其利用受试者工作特征(ROC)曲线来检测所述数据采集模块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、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计算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、窦卵泡计数(AFC)的数据；计算卵巢储备功能，其利用受试者工作特征(ROC)曲线来检测所述数据采集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、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，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，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，窦卵泡计数(AFC)是指阴道B超计数女性受试者月经第2天时的两个卵巢中直径为2-10mm的所有可见卵泡的个数。
其中，抗缪勒氏管激素(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。
窦卵泡计数(AFC)是指月经第2天时两个卵巢中直径为2-10mm的所有可见卵泡的个数。AFC可以通过超声波对卵泡测量和计数。
在本申请的一个具体的系统或方法中，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％。
在本申请的一个具体的系统或方法中，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。
在本申请的一个具体的系统或方法中，如上所述，需要计算卵巢储备功能，其利用受试者工作特征(ROC)曲线来检测所述数据采集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、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当前卵巢低反应概率(p 0)，具体来说，上述年龄的切点值为35岁，抗缪勒氏管激素(AMH)水平的切点值为1.2ng/ml，卵泡刺激素(FSH)水平的切点值为8IU/L，以及窦卵泡计数(AFC)的切点值为8。在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、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二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 PCTCN2020102090-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，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
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、窦卵泡计数(AFC)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、窦卵泡计数(AFC)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、窦卵泡计数(AFC)的数据进行变量转换，将其转换成二分类变量。
如果某一受试者的年龄取值在小于35岁，则其二分类变量之后，带入公式一中的年龄参数的取值为0，如果年龄取值在大于等于35岁，则其二分类变量之后，带入公式一中的年龄参数的取值为1；如果某一受试者的抗缪勒氏管激素(AMH)水平低于1.2ng/ml，则其二分类变量之后，带入公式一中的AMH参数的取值为1，如果某一受试者的抗缪勒氏管激素(AMH)水平大于等于1.2ng/ml，则其二分类变量之后，带入公式一中的AMH参数的取值为0，如果某一受试者的卵泡刺激素(FSH)水平低于8IU/L，则其二分类变量之后，带入公式一中的FSH参数的取值为1，如果某一受试者的卵泡刺激素(FSH)水平低于8IU/L，则其二分类变量之后，带入公式一中的FSH参数的取值为1，如果某一受试者的卵泡刺激素(FSH)水平大于等于8IU/L，则其二分类变量之后，带入公式一中的FSH参数的取值为0，如果某一受试者的窦卵泡计数(AFC)低于8，则其二分类变量之后，带入公式一中的AFC参数的取值为1，如果某一受试者的窦卵泡计数(AFC)水平大于等于8，则其二分类变量之后，带入公式一中的AFC参数的取值为0。
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、窦卵泡计数(AFC)，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本人已发表的文章(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，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对人群进行分类，一共分成四类。同时对这些受试者 的妊娠结局情况也进行总结，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1所示。基于表1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之前的卵巢储备评估模型，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。逻辑回归分析表明，年龄增长的人发生DOR的风险更高。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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。如上所述，age2-age1的结果即给出了从当前年龄发展到受试者出现卵巢储备明显下降导致生育力明显下降时的年限。
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 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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。如上所述，age3-age1的结果即给出了从当前年龄发展到受试者卵巢储备接近耗竭导致生育力接近耗竭的年限。
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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。如上所述，age4-age1的结果即给出了从当前年龄发展到受试者卵巢储备开始下降导致生育力下降开始的年限。
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以及围绝经期健康管理具有重要意义，是降低育龄妇女不育率的有效方法。
实施例
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)。
分类变量确定和计算卵巢低反应概率模型的构建
在本申请在先的研究中，利用SAS JMP Pro软件(版本14.2)，对包括年龄、BMI、不孕原因、月经第2天阴道B超计数的AFC个数、月经第2天的FSH水平、月经任意一天的AMH水平、月经2天的LH水平以及月经2天的E 2水平与作为结果变量的卵巢低反应的相关性进行分析。当设定的结果变量是卵巢低反应时，确认了年龄、AMH水平、FSH水平和AFC个数与卵巢储备情况显著相关(在利用SAS JMP Pro软件进行计算时，所有P值小于0.05被认为具有统计学意义)。
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。
针对本实施例的16820受试者，首先，利用ROC(受试者工作特征)曲线 的方法确定与卵巢低反应的相关四个连续变量的分界点(cut-off point)，并根据该分界点(cut-off point)，进行变量变化，将连续变量变成了二分类变量。通过使用ROC曲线的切点值将年龄、AMH、FSH和AFC转换成二分类变量。具体来说，采用ROC曲线确定年龄、AMH、FSH、AFC的分界点，并分别确定该分界点的切点值。基于ROC曲线的分析，结果找到年龄、AMH、FSH、AFC的切点值分别为35岁、1.2、8、8。可以确认四个指标的结果分别依次如下年龄，AMH，FSH和AFC的切点值分别为35岁，1.2ng/ml，8IU/L和8，由此将年龄分为<＝35和>35，AMH分为<＝1.2和>1.2，FSH分为<＝8和>8，AFC分为<＝8和>8，从而依据上述标准将年龄、AMH、FSH和AFC转换成二分类变量。
针对16820受试者，按照上述截点值将年龄、AMH、FSH和AFC转换成二分类变，随后按照申请人在先构建的模型(参见申请人在先的中国专利201811516206.4)来计算受试者的卵巢储备功能的参数(p)，在该中国专利中，申请人利用了561个受试者，利用SAS软件和R软件，采用二元逻辑回归模型构建了如下模型，该模型的构建和参数的获取可以详细参见中国专利201811516206.4。
在中国专利201811516206.4中最终建立的模型logistic预测模型为：
Figure PCTCN2020102090-appb-000013
根据表1的数据可以确定，i的范围为-1.786～-0.499，最优选i＝-1.143，
a的范围为0.063-1.342，最优选a＝0.703，b的范围为-2.542～-1.056，最优选b＝-1.799，c的范围为0.548～1.838，最优选c＝1.193，d的范围为-2.133～-0.51，最优选d＝-1.322。由此，根据上述公式一可以基于对某一受试者的年龄、月经周期静脉血中的抗缪勒氏管激素浓度，月经周期静脉血中的卵泡刺激素浓度，月经周期的两个卵巢中直径为2-10mm的所有可见卵泡的个数来计算这个受试者的卵巢低反应概率p。
在中国专利201811516206.4中，采用的是月经2-4天的静脉血中的抗缪勒氏管激素浓度，月经2-4天的静脉血中的卵泡刺激素浓度，月经2-4天时的两个卵巢中直径为2-8mm的所有可见卵泡的个数，而在本申请中，由于人群变化和检测设备的调整，具体实施例中采用的是月经第2天阴道B超计 数的AFC个数、月经第2天的FSH水平、月经任意一天的AMH水平，同时，同时如上所述，基于16820名受试者的ROC曲线，调整了将年龄、AMH、FSH和AFC转换成二分类变量的截点值。
由此，根据上述公式一可以基于对某一受试者的年龄、月经2-4天的静脉血中的抗缪勒氏管激素浓度，月经2-4天的静脉血中的卵泡刺激素浓度，月经2-4天时的两个卵巢中直径为2-10mm的所有可见卵泡的个数来计算这个受试者的卵巢低反应概率p。
为了进一步明确，在本实施例中，将上述计算的受试者的卵巢低反应概率p成为受试者当前的卵巢低反应概率(p 0)，则与上述公式相同，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 PCTCN2020102090-appb-000014
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，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
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)
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1所示。基于表1，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本人已发表的文章(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，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1所示。表1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1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1.四个卵巢储备组的临床妊娠率和活产率。
Figure PCTCN2020102090-appb-000015
ET：胚胎移植
基于表1，将人群分成四组，即A组、B组、C组和D组。表1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1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 PCTCN2020102090-appb-000016
其中，利用逻辑曲线参数拟合的数据如下表2所示。
表2
Figure PCTCN2020102090-appb-000017
其中，公式二中，x表示生长速率，基于表2可以看出，x为选自0.219-0.265中的任意数值，优选x为0.242，y表示拐点值，基于表2可以看出，y为选自40.905～41.733中的任意数值，优选y为41.319。
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 PCTCN2020102090-appb-000018
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0.219-0.265中的任意数值，优选x为0.242。
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 PCTCN2020102090-appb-000019
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0.219-0.265中的任意数值，优选x为0.242。
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 PCTCN2020102090-appb-000020
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0.219-0.265中的任意数值，优选x为0.242。
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16820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3所示：
表3.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明显下降(低反应概率50％)所需的时间。
Figure PCTCN2020102090-appb-000021
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3所示，对于第一组的人群，预测其出现生育力出现显著下降的年限是13.4年，而对于第二组人群，预测其出现生育力显著下降的年限是6.9年，而对于第三组人群，预测其出现生育力显著下降的年限是3.3年，而对于第四组人群，预测其出现DOR的年限是-0.3年，即其应该已经出现了生育力显著下降。
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 (10)

  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、窦卵泡计数(AFC)的数据；
    计算卵巢储备功能的模块，其利用受试者工作特征(ROC)曲线来检测所述数据采集模块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、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，所述窦卵泡计数(AFC)是指阴道B超计数女性受试者月经第2天时的两个卵巢中直径为2-10mm的所有可见卵泡的个数。
  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1～4中任一项所述的系统，其中，
    所述年龄的切点值为35岁，所述抗缪勒氏管激素(AMH)水平的切点值为1.2ng/ml，所述卵泡刺激素(FSH)水平的切点值为8IU/L，以及所述窦卵泡计数(AFC)的切点值为8。
  6. 根据权利要求5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、受试者卵泡刺激素(FSH)水平、受试者窦卵泡计数(AFC)的数据转换成的二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。
  7. 根据权利要求6所述的系统，其中，
    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 PCTCN2020102090-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，i为选自-1.786～-0.499中的任意数值，a为选自0.063-1.342中的任意数值，b为选自-2.542～-1.056中的任意数值，c为选自0.548～1.838中的任意数值，d为选自-2.133～-0.51中的任意数值，其中优选i＝-1.143，优选a＝0.703，优选b＝-1.799，优选c＝1.193，优选d＝-1.322。
  8. 根据权利要求7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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
  9. 根据权利要求7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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
  10. 根据权利要求7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 PCTCN2020102090-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0.219-0.265中的任意数值，优选x为0.242。
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