Integrated evaluation system for ovarian reserve, endocrine age and senescence trend for female fertility planning

Abstract

The invention relates to an ovarian reserve, endocrine age and aging trend integrated evaluation system for female fertility planning, and the system comprises a data collection module which is used for obtaining the data of the age and the anti-mullerian hormone (AMH) level of a subject; the module for calculating the low ovarian response function is used for calculating the data acquired in the data acquisition module so as to calculate the low ovarian response probability (p) of the subject and further converting the low ovarian response probability into a standardized ovarian reserve score; a module for calculating the endocrine age of the subject, wherein the endocrine age of the subject is calculated by using the low ovarian response probability of the subject; and the module is used for predicting the age of the change of the ovarian reserve of the subject, and the endocrine age of the subject is used for predicting the age limit or age of the new change of the ovarian reserve of the subject. By using the system and the method of the invention, the female at the childbearing age can be timely helped to know the optimal childbearing opportunity.

Description

Technical Field

[0001] The present application relates to a system for optimizing the assessment of a subject's ovarian reserve function. The system can be used to assess the subject's own ovarian reserve function and, based on the subject's current ovarian reserve status, assess whether the subject has reached a specific ovarian reserve status, such as near-exhaustion of the ovarian reserve, early fertility decline due to ovarian reserve decline, and the number of years required for a significant decline in fertility due to ovarian reserve decline (or the predicted age at which different individuals will reach the above-mentioned ovarian reserve status). Background Art

[0002] The number of primordial follicles within the ovarian cortex is called ovarian reserve. It reflects the ovaries' ability to produce healthy, fertile eggs and is the most important indicator of ovarian function. Generally speaking, a greater number of primordial follicles indicates better quality and a higher chance of pregnancy.

[0003] However, many women of childbearing age are unaware of the significant variability in ovarian reserve—the number of primordial follicles in the ovarian cortex, which ranges from tens of thousands to millions at birth. In our clinical practice, we observe that some women maintain a good ovarian reserve into their forties, while others face diminished ovarian reserve (DOR) or even depletion in their twenties. To improve infertility and achieve pregnancy, an increasing number of couples are seeking assisted reproductive technology (ART). However, not all couples benefit from ART, and its role in DOR or perimenopausal women is extremely limited because their fertility has already significantly declined or is near depletion, with few or no ovarian follicles. International consensus exists that even expensive ovulation induction treatments cannot improve fertility in these women.

[0004] Although the number and quality of ovarian follicles undergo profound changes with age, the process of ovarian aging has received insufficient attention. People often don't discover their fertility may be declining until they experience irregular menstruation or menopause. By the time these signs appear, their fertility is already extremely low, and assisted reproductive technology can no longer improve fertility. Early fertility decline caused by diminished ovarian reserve often occurs early, but the long-standing lack of clear methods for assessing ovarian reserve has caused many women of childbearing age to miss their optimal reproductive opportunities. Summary of the Invention

[0005] As mentioned above, assessing a patient's current ovarian reserve is a crucial task for clinicians and others. By assessing ovarian reserve, it is possible to predict a patient's ovarian responsiveness, a crucial clinical outcome during ovarian stimulation treatment. Previously, clinicians often relied on their own experience, based on factors such as age, body mass index, endocrine factor levels, and antral follicle count, making these assessments somewhat subjective. Our system can accurately assess the ovarian reserve of prospective patients, assisting clinicians in developing more targeted treatment plans.

[0006] In summary, it is known that the determining factor of ovarian responsiveness is ovarian reserve function, but the inventors of this application have reversed their thinking and used expected ovarian responsiveness to assess ovarian reserve function. The inventors of this application first obtained the expected probability of low ovarian response based on the patient's basic conditions, and then grouped the subject's current ovarian reserve function according to the system's pre-stored default ovarian reserve function grouping parameters, thereby determining the level of their ovarian reserve function and assessing the ovarian reserve level or ovarian youth level.

[0007] The system for assessing the ovarian reserve function of a subject developed by the inventors can be used to calculate the subject's ovarian low response probability (p), that is, the subject's current ovarian low response probability, or the subject's current ovarian reserve status.

[0008] Furthermore, there's a 'fixed interval' hypothesis internationally, which states that there's a fixed time interval between different ovarian reserve states. This means that the rate of follicle depletion is roughly constant across the population. For example, the time interval from early ovarian reserve decline to significant decline and then to ovarian reserve depletion is roughly the same for different individuals. This hypothesis is primarily based on the fact that the length of the menstrual cycle is generally stable across the population, typically 28 days. Based on this hypothesis, in this application, the inventors hypothesize that the shape of the curve depicting the time interval between ovarian reserve growth and age change is relatively fixed.

[0009] Based on this assumption, that is, the rate of ovarian reserve status change with age is fixed across the entire reproductive population, the inventors of this application attempted to predict, based on the current ovarian reserve status and the rate of ovarian reserve depletion (DOR growth rate with age), when a specific woman will develop early ovarian reserve decline (a 25% probability of low response), further progress to ovarian reserve decline leading to a significant decline in fertility (a 50% probability of low response), or progress to near ovarian reserve depletion leading to near fertility depletion (a 95% probability of low response). The inventors hope to develop a method and system to help women predict the time (or age) to reach their desired ovarian reserve status based on their current ovarian reserve status. This will be of great significance for women to rationally plan their fertility and may be an effective way to reduce the infertility rate in women of childbearing age.

[0010] Specifically, this application involves the following:

[0011] 1. A system for predicting ovarian aging in a subject, comprising:

[0012] a data acquisition module for acquiring data on the age and anti-Mullerian hormone (AMH) level of the subject;

[0013] A module for calculating ovarian hyporesponsiveness function, which is used to calculate the above data acquired in the data acquisition module, thereby calculating the subject's ovarian hyporesponsiveness probability (p), and further converting the ovarian hyporesponsiveness probability into a standardized ovarian reserve score;

[0014] a module for calculating the subject's endocrine age, which calculates the subject's endocrine age using the subject's probability of low ovarian response; and

[0015] The module predicts the age at which the ovarian reserve of the subject changes, which uses the subject's endocrine age to predict the year or age at which the subject will experience new changes in ovarian reserve.

[0016] 2. A system according to claim 1, wherein:

[0017] For women with regular menstruation, the anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in the venous blood of the female subject on any day;

[0018] For women with irregular menstruation, the anti-Mullerian hormone (AMH) level refers to the highest value of the anti-Mullerian hormone concentration in the venous blood of the female subject during multiple consecutive venous blood draws during the menstrual cycle.

[0019] 3. A system according to claim 1, wherein:

[0020] The poor ovarian response refers to the actual number of eggs retrieved by the subject being less than 5.

[0021] 4. A system according to claim 1, wherein

[0022] In the module for calculating ovarian hyporesponsiveness, the probability of ovarian hyporesponsiveness (p) of the subject is calculated based on a logistic regression model using the subject's age and anti-Mullerian hormone (AMH) level.

[0023] 5. A system according to claim 1, wherein

[0024] In the module for calculating ovarian poor response function, the ovarian reserve score is calculated using the following conversion formula:

[0025] Ovarian reserve score = (1 - probability of poor ovarian response) × 100.

[0026] 6. A system according to claim 1, wherein

[0027] In the module for calculating the endocrine age of the subject, the subject's endocrine age is calculated using the subject's probability of low ovarian response based on any one of a two-parameter logistic regression model, a two-parameter Probit model, a three-parameter Gompertz model, or a Weibul curve.

[0028] 7. A system according to claim 6, wherein:

[0029] The threshold in the two-parameter logistic regression model, two-parameter Probit model, three-parameter Gompertz model, or Weibul curve was set at 0.15.

[0030] 8. The system according to item 1, wherein the calculated probability of poor ovarian response and standardized ovarian reserve score of the subject include the following five situations:

[0031] A low ovarian response probability of less than 5% is converted to an ovarian reserve score greater than 95 points;

[0032] The probability of poor ovarian response greater than or equal to 5% and less than 25% is converted into an ovarian reserve score greater than 75 points and less than or equal to 95 points;

[0033] The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted into an ovarian reserve score greater than 50 points and less than or equal to 75 points;

[0034] The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted into an ovarian reserve score greater than 25 points and less than or equal to 50 points;

[0035] A probability of poor ovarian response greater than or equal to 75% and less than 84% is converted into an ovarian reserve score greater than or equal to 16 points and less than or equal to 25 points.

[0036] 9. A system according to claim 4, wherein:

[0037] In the module for calculating ovarian hyporesponsiveness, the probability (p) of the subject's ovarian hyporesponsiveness is calculated by performing a quadratic transformation on the subject's age and a cubic transformation on the subject's anti-Mullerian hormone (AMH) level data.

[0038] 10. The system of claim 9, wherein:

[0039] The formula for calculating the probability of low ovarian response (p) of a subject is as follows:

[0040]

[0041] (Formula 1)

[0042] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject,

[0043] e represents a natural constant;

[0044] Wherein, a, b, c, d, f, and g are unitless parameters; age represents the age of the subject, and AMH represents the anti-Mullerian hormone (AMH) level of the subject;

[0045] In the module for calculating ovarian reserve function, the subject's age and the subject's anti-Mullerian hormone (AMH) level are taken as logarithms and substituted into Formula 1 for calculation.

[0046] 11. A system according to claim 10, wherein:

[0047] a is any value selected from -3.830 to -2.929,

[0048] b is any value selected from 0.042 to 0.069,

[0049] c is any value selected from 0.0017 to 0.0050,

[0050] d is any value selected from -1.490 to -1.327,

[0051] f is any value selected from 0.250 to 0.373,

[0052] g is any value selected from 0.091 to 0.130.

[0053] 12. A system according to claim 5, wherein:

[0054] In the module for calculating the endocrine age of the subject, according to the probability of ovarian low response, the endocrine age (age) is calculated by reverse deduction based on the two-parameter logistic curve using the following formula 2. Endocrine ):

[0055] (Formula 2)

[0056] Where p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0057] Where a1 is a fixed value, preferably 37.553; b1 is a fixed value, preferably 0.28

[0058] 13. A system according to claim 12, wherein:

[0059] In the module for calculating the endocrine age of the subject, the standard error of the endocrine age (se age ) to calculate the endocrine age (age Endocrine ),

[0060] se age =

[0061] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject; a2, b2 and c2 are unitless parameters;

[0062] Among them, a2 is a fixed value, the value is 0.283; b2 is a fixed value, the value is 0.012; c2 is a fixed value, the value is 0.168;

[0063] The lower and upper limits of the 95% confidence interval for calculating endocrine age are:

[0064] .

[0065] 14. A system according to claim 1, wherein

[0066] In the module for predicting the age at which the subject's ovarian reserve changes, the following formula 3 is used to calculate the number of years from the subject's current ovarian reserve to the time when the ovarian reserve significantly decreases, leading to a significant decline in fertility, that is, the number of years when the probability of ovarian hyporesponsiveness is 50%:

[0067] Based on the endocrine age, the number of years until the age of ovarian reserve 50 points (D1) was calculated, which was the difference between the age with p = 0.5 obtained from the logistic curve and the endocrine age;

[0068] (Formula 3)

[0069] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0070] Preferably, a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine You can choose any value between the lower and upper limits of the 95% confidence interval of formula 2, and further optimize age Endocrine is the calculated value of Formula 2.

[0071] 15. The system according to claim 14, wherein the age at which the ovarian reserve is 50 points is calculated by the following formula: DOR ):

[0072] age DOR = age chronological + D1

[0073] Among them, age chronological represents the current age of the subject, and D1 is the D1 value calculated by formula 3.

[0074] 16. A system according to claim 1, wherein

[0075] In the module for predicting the age at which the subject's ovarian reserve changes, the following formula 4 is used to calculate the number of years from the subject's current ovarian reserve to the onset of perimenopause, that is, the number of years when the probability of ovarian low response is 95%:

[0076] Based on the endocrine age, the number of years until perimenopause (D2) was calculated, which was the difference between the age at which p was 0.95 obtained from the logistic curve and the endocrine age:

[0077] (Formula 4)

[0078] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0079] a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine Any value between the lower and upper limits of the 95% confidence interval of formula 2 can be selected to further Endocrine The calculated value of formula 2 is preferred.

[0080] 17. The system according to claim 16, wherein the age at onset of perimenopause is calculated by the following formula 围绝经 ):

[0081] age 围绝经 = age chronological + D2

[0082] Among them, age chronological represents the current age of the subject, and D2 is the D2 value calculated by formula 4.

[0083] 18. A method for predicting ovarian aging in a subject, comprising:

[0084] a data collection step, which obtains data on the subject's age and anti-Mullerian hormone (AMH) level;

[0085] a step of calculating ovarian hyporesponsiveness function, which calculates the data obtained in the data collection step to calculate the subject's ovarian hyporesponsiveness probability (p), and further converts the ovarian hyporesponsiveness probability into a standardized ovarian reserve score;

[0086] a step of calculating the subject's endocrine age, which calculates the subject's endocrine age using the subject's probability of low ovarian response; and

[0087] The step of predicting the age at which the subject's ovarian reserve changes, using the subject's endocrine age to predict the year or age at which the subject will experience new changes in ovarian reserve.

[0088] 19. The method of claim 18, wherein

[0089] For women with regular menstruation, the anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in the venous blood of the female subject on any day;

[0090] For women with irregular menstruation, the anti-Mullerian hormone (AMH) level refers to the highest value of the anti-Mullerian hormone concentration in the venous blood of the female subject during multiple consecutive venous blood draws during the menstrual cycle.

[0091] 20. The method of claim 18, wherein

[0092] The poor ovarian response refers to the actual number of eggs retrieved by the subject being less than 5.

[0093] 21. The method of claim 18, wherein

[0094] In the step of calculating the ovarian hyporesponsiveness function, the probability (p) of the subject's ovarian hyporesponsiveness is calculated based on a logistic regression model using the subject's age and anti-Mullerian hormone (AMH) level.

[0095] 22. The method of claim 18, wherein

[0096] In the step of calculating ovarian poor response function, the ovarian reserve score is calculated using the following conversion formula:

[0097] Ovarian reserve score = (1 - probability of poor ovarian response) × 100.

[0098] 23. The method of claim 18, wherein

[0099] In the step of calculating the subject's endocrine age, the subject's endocrine age is calculated using the subject's probability of low ovarian response based on any one of a two-parameter logistic regression model, a two-parameter Probit model, a three-parameter Gompertz model, or a Weibul curve.

[0100] 24. The method of claim 23, wherein:

[0101] The threshold in the two-parameter logistic regression model, two-parameter Probit model, three-parameter Gompertz model, or Weibul curve was set at 0.15.

[0102] 25. The method according to claim 18, wherein the calculated probability of poor ovarian response and standardized ovarian reserve score of the subject include the following five situations:

[0103] A low ovarian response probability of less than 5% is converted to an ovarian reserve score greater than 95 points;

[0104] The probability of poor ovarian response greater than or equal to 5% and less than 25% is converted into an ovarian reserve score greater than 75 points and less than or equal to 95 points;

[0105] The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted into an ovarian reserve score greater than 50 points and less than or equal to 75 points;

[0106] The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted into an ovarian reserve score greater than 25 points and less than or equal to 50 points;

[0107] A probability of poor ovarian response greater than or equal to 75% and less than 84% is converted into an ovarian reserve score greater than or equal to 16 points and less than or equal to 25 points.

[0108] 26. The method of claim 21, wherein

[0109] In the step of calculating the ovarian hyporesponsiveness, the probability (p) of the subject's ovarian hyporesponsiveness is calculated by performing a quadratic transformation on the subject's age and a cubic transformation on the subject's anti-Mullerian hormone (AMH) level data.

[0110] 27. The method of claim 26, wherein:

[0111] The formula for calculating the probability of low ovarian response (p) of a subject is as follows:

[0112]

[0113] (Formula 1)

[0114] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject,

[0115] e represents a natural constant;

[0116] Wherein, a, b, c, d, f, and g are unitless parameters; age represents the age of the subject, and AMH represents the anti-Mullerian hormone (AMH) level of the subject;

[0117] In the step of calculating the ovarian reserve function, the age of the subject and the anti-Mullerian hormone (AMH) level of the subject are taken as the logarithm and substituted into Formula 1 for calculation.

[0118] 28. The method of claim 27, wherein:

[0119] a is any value selected from -3.830 to -2.929,

[0120] b is any value selected from 0.042 to 0.069,

[0121] c is any value selected from 0.0017 to 0.0050,

[0122] d is any value selected from -1.490 to -1.327,

[0123] f is any value selected from 0.250 to 0.373,

[0124] g is any value selected from 0.091 to 0.130.

[0125] 29. The method of claim 22, wherein:

[0126] In the step of calculating the endocrine age of the subject, according to the probability of ovarian low response, the endocrine age (age) was calculated by reverse deduction based on the two-parameter logistic curve using the following formula 2. Endocrine ):

[0127] (Formula 2)

[0128] Where p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0129] Where a1 is a fixed value, preferably 37.553; b1 is a fixed value, preferably 0.28

[0130] 30. The method of claim 29, wherein

[0131] In the step of calculating the endocrine age of the subject, the standard error of the endocrine age (se age ) to calculate the endocrine age (age Endocrine ),

[0132]

[0133] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject; a2, b2 and c2 are unitless parameters;

[0134] Among them, a2 is a fixed value, the value is 0.283; b2 is a fixed value, the value is 0.012; c2 is a fixed value, the value is 0.168;

[0135] The lower and upper limits of the 95% confidence interval for calculating endocrine age are:

[0136] .

[0137] 31. The method of claim 18, wherein

[0138] In the step of predicting the age at which the subject's ovarian reserve changes, the following formula 3 is used to calculate the number of years from the subject's current ovarian reserve to the time when the ovarian reserve significantly decreases, leading to a significant decline in fertility, that is, the number of years when the probability of ovarian poor response is 50%:

[0139] Based on the endocrine age, the number of years until the age of ovarian reserve 50 points (D1) was calculated, which was the difference between the age with p = 0.5 obtained from the logistic curve and the endocrine age;

[0140] (Formula 3)

[0141] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0142] Preferably, a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine You can choose any value between the lower and upper limits of the 95% confidence interval of formula 2, and further optimize age Endocrine is the calculated value of Formula 2.

[0143] 32. The method according to claim 31, wherein the age at which the ovarian reserve reaches 50 points is calculated by the following formula: DOR ):

[0144] age DOR = age chronological + D1

[0145] Among them, age chronological represents the current age of the subject, and D1 is the D1 value calculated by formula 3.

[0146] 33. The method of claim 18, wherein

[0147] In the step of predicting the age at which the subject's ovarian reserve changes, the following formula 4 is used to calculate the number of years from the subject's current ovarian reserve to the onset of perimenopause, that is, the number of years when the probability of ovarian low response is 95%:

[0148] Based on the endocrine age, the number of years until perimenopause (D2) was calculated, which was the difference between the age at which p was 0.95 obtained from the logistic curve and the endocrine age:

[0149] (Formula 4)

[0150] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0151] a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine Any value between the lower and upper limits of the 95% confidence interval of formula 2 can be selected to further Endocrine The calculated value of formula 2 is preferred.

[0152] 34. The method of claim 33, wherein the age at onset of perimenopause is calculated by the following formula: 围绝经 ):

[0153] age 围绝经 = age chronological + D2

[0154] Among them, age chronological represents the current age of the subject, and D2 is the D2 value calculated by formula 4.

[0155] Effects of the Invention

[0156] The system of the present application can identify individuals with diminished ovarian reserve (DOR) at an early stage. Using the system and method of the present application, subjects can be helped to predict the years in which their ovarian reserve will undergo critical changes, and can be advised to begin fertility attempts as early 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 sufficient attention. People often don't discover that their fertility may be declining until they experience irregular menstruation or menopause. By the time these signs appear, their fertility is already extremely low, and assisted reproductive technology (ART) cannot be used to improve fertility. Early fertility decline due to diminished ovarian reserve typically occurs relatively early, but the long-standing lack of clear ovarian reserve assessment methods has resulted in many women of childbearing age missing out on their optimal reproductive opportunities. However, using the system and method of the present application, subjects can be accurately predicted when their ovarian reserve will begin to decline, leading to a decline in fertility; when their ovarian reserve will significantly decline, leading to a significant decline in fertility; or when their ovarian reserve will be near exhaustion, leading to near-exhaustion of fertility. Using such a system and method can promptly help women of childbearing age understand their optimal reproductive opportunities. BRIEF DESCRIPTION OF THE DRAWINGS

[0157] Various other advantages and benefits of the present application will become apparent to those of ordinary skill in the art, upon reading the following detailed description of the preferred embodiment. The accompanying drawings are included to provide a better understanding of the preferred embodiment and are not to be considered as limitations. It should be readily understood that the drawings are not to scale and that the drawings are merely intended to depict the general structure of the application. It should be readily understood that the drawings are not to scale and that the drawings are merely intended to depict the general structure of the application.

[0158] Figure 1 Calibration of each model is shown. Where (A) training cohort (2017-2019); (B) test cohort (2020-2021). Top panels cover the entire 0-1.0 range; bottom panels zoom into 0-0.25. Dotted black line = ideal. Model-0 overestimates risk <0.60; Model-1 improves fit; Model-2 is closest to ideal line.

[0159] Figure 2 Declining female reproductive capacity trajectories with age are shown. Where (A) conceptual schematic showing four key milestones - low fertility, infertility, irregular menses, and menopause - modeled as sigmoid functions of age. 50% probability lines intersect these curves at approximately 31, 41, 46, and 51 years, indicating gradual shifts in reproductive events with age. (B) Empirical ovarian aging curve in 31923 first-cycle ART patients: observed proportion of reduced ovarian reserve (DOR, black dots) with age, fitted with a two-parameter logistic. High agreement confirms the sigmoid rise in DOR risk, which is Figure 2 the basis of the schematic in panel A.

[0160] Figure 3 Intra-cycle AMH dynamics and their diagnostic performance are shown. (A) Serum AMH distributions measured on cycle days 2, 6, and 12 (first row) are highly right-skewed; log-transformation (second row) normalizes the data, as shown by histograms, density curves, and accompanying box-and-whisker plots. (B) Least squares (LS) means of Ln(AMH) gradually decrease throughout the follicular phase (day 2 > day 6 > day 12); error bars indicate 95% CIs, and the inserted table lists pairwise differences. (C) ROC curves comparing POR prediction models that include AMH sampled on day 2, day 6, or day 12. DETAILED DESCRIPTION

[0161] The specific embodiments of the present application will be described in more detail below. However, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0162] It should be noted that certain words are used in the specification and claims to refer to specific components. Those skilled in the art should understand that technicians may use different nouns to refer to the same component. This specification and claims do not use the difference in nouns as a way to distinguish components, but use the difference in the functions of the components as the criterion for distinction. As mentioned throughout the specification and claims, "including" or "comprising" is an open term, so it should be interpreted as "including but not limited to". The subsequent description of the specification is a preferred embodiment of the present application, but the description is based on the general principles of the specification and is not intended to limit the scope of the present application. The scope of protection of this application shall be as defined by the attached claims.

[0163] In this application, ovarian reserve refers to the number of primordial follicles within the ovarian cortex. It reflects the ovaries' ability to produce healthy, fertile eggs and is the most important indicator of ovarian function. Generally speaking, a higher number of primordial follicles indicates better quality and a higher chance of pregnancy.

[0164] Ovarian hyporesponsiveness, also known as decreased ovarian reserve (DOR) in this application, refers to the number of oocytes retrieved on the day of egg retrieval in the reproductive cycle being less than 5 (i.e., 0-4).

[0165] However, the number of primordial follicles cannot be assessed non-invasively and can only be assessed by the number of follicles mobilized in each menstrual cycle. Too few follicles mobilized in the IVF-ET cycle (ovarian hyporesponsiveness) indicates a decrease in ovarian reserve function, that is, youthfulness.

[0166] Age 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 for women under 30 years old is about 26%, while the IVF success rate for women aged 37 and above is only 9%.

[0167] The mechanisms by which ovarian reserve capacity declines with age are as follows. (1) A decrease in the number of follicles. Primordial follicles appear after embryonic sex differentiation, when the number of follicles is at its highest. After puberty, follicles begin to mature. With the completion of ovulation, a large number of follicles that were recruited but not released shrink and disappear, forming a corpus luteum. The number of follicles decreases continuously with age: in humans, the number of follicles is at its highest at 20 weeks of age, approximately 6 million. This decreases to 700,000-2 million follicles during the neonatal period, approximately 40,000 during puberty, and only a few thousand at the onset of menopause, until it is completely depleted. (2) A decrease in egg quality. Embryo quality is primarily determined by egg quality. Advanced age can increase the probability of oocyte aneuploidy, the risk of mitochondrial dysfunction, the loss of oocyte polarity, and epigenetic changes in oocytes. (3) Endocrine factors. The hypothalamic-pituitary-ovarian axis regulates women's menstrual cycles 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 capacity. With aging, ovarian reserve decreases, reducing the number of recruitable follicles. Consequently, the concentrations of AMH and inhibin B secreted by the ovarian ovary also decrease. Inhibin B negatively regulates pituitary FSH secretion. Decreased inhibin B levels lead to increased FSH secretion during the luteal phase. This premature increase in FSH promotes the growth of new follicles and E2 secretion, ultimately shortening the menstrual cycle. Increased serum FSH levels and decreased inhibin B levels reduce follicle sensitivity to FSH, indicating a decrease in the number of antral follicles available for recruitment. The menstrual cycle is a reflection of ovarian reserve and fertility. A shortened menstrual cycle with advanced age, with a reduction of 2-3 days in the menstrual cycle, is a sensitive indicator of reproductive aging, indicating premature initiation of follicular growth (elevated FSH levels) and a decrease in the primordial follicle reserve, or in other words, youthfulness.

[0168] In response to the problems existing in the prior art, the present application provides a system for predicting ovarian aging in a subject, which comprises:

[0169] a data acquisition module for acquiring data on the age and anti-Mullerian hormone (AMH) level of the subject;

[0170] A module for calculating ovarian hyporesponsiveness function, which is used to calculate the above data acquired in the data acquisition module, thereby calculating the subject's ovarian hyporesponsiveness probability (p), and further converting the ovarian hyporesponsiveness probability into a standardized ovarian reserve score;

[0171] a module for calculating the subject's endocrine age, which calculates the subject's endocrine age using the subject's probability of low ovarian response; and

[0172] The module predicts the age at which the ovarian reserve of the subject changes, which uses the subject's endocrine age to predict the year or age at which the subject will experience new changes in ovarian reserve.

[0173] In the data collection module, different methods are used to measure anti-Mullerian hormone (AMH) levels, depending on whether the subject has regular menstruation. If the menstrual cycle is irregular, AMH levels may fluctuate significantly within and between cycles. Therefore, relying on a single test may not accurately reflect the true ovarian reserve.

[0174] Among them, for women with regular menstruation, the anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in the venous blood of the female subject on any day; for women with irregular menstruation, the anti-Mullerian hormone (AMH) level refers to the highest value of the anti-Mullerian hormone concentration in the venous blood of the female subject in multiple consecutive venous blood draws during the menstrual cycle.

[0175] In this application, regular menstruation refers to a menstrual cycle of 21-35 days, with each menstruation lasting 3 to 7 days, and regular bleeding volume and menstrual symptoms; irregular menstruation refers to a menstrual cycle shorter than 21 days or longer than 35 days, or an irregular menstrual interval.

[0176] In some embodiments, the input range of the AMH concentration value is 0.06-41.4 ng / ml.

[0177] In the module for calculating ovarian hyporesponsiveness, the probability of ovarian hyporesponsiveness (p) of the subject is calculated using the subject's age and anti-Mullerian hormone (AMH) level based on a logistic regression model. Specifically, the probability of ovarian hyporesponsiveness (p) of the subject is calculated by performing a quadratic transformation on the subject's age and a cubic transformation on the subject's anti-Mullerian hormone (AMH) level data.

[0178] In some embodiments, the formula for calculating the probability of poor ovarian response (p) of a subject is as follows:

[0179]

[0180] (Formula 1)

[0181] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject,

[0182] e represents a natural constant;

[0183] Wherein, a, b, c, d, f, and g are unitless parameters; age represents the age of the subject, and AMH represents the anti-Mullerian hormone (AMH) level of the subject;

[0184] In the module for calculating ovarian reserve function, the subject's age (age) and the subject's anti-Mullerian hormone (AMH) level are taken as logarithms and substituted into Formula 1 for calculation.

[0185] In some embodiments, a is any value selected from -3.830 -2.929, b is any value selected from 0.042-0.069, c is any value selected from 0.0017-0.0050, d is any value selected from -1.490 -1.327, f is any value selected from 0.250-0.373, g is any value selected from 0.091-0.130.

[0186] In the present application, the purpose of converting the probability of poor ovarian response into an ovarian reserve score is to convert the predicted risk of poor response into an intuitive score indicator. Originally, the probability of poor response reflects the risk of obtaining a low number of oocytes during the process of ovulation induction, and after the conversion, a high score means a low risk and a good ovarian reserve; a low score indicates a higher risk and a poor ovarian function. Through the conversion, a lower probability of poor response will correspond to a higher score, which intuitively shows the pros and cons of the individual ovarian reserve level. In this way, the score is more in line with clinical intuition, which helps doctors quickly judge the ovarian function status of individuals, and also facilitates the integration with other reproductive indicators, providing a reliable basis for risk stratification and individualized treatment.

[0187] In some embodiments, the ovarian reserve score is calculated by the following conversion formula:

[0188] Ovarian reserve score = (1 - probability of poor ovarian response) x 100.

[0189] In some embodiments, the calculated probability of poor ovarian response and standardized ovarian reserve score of the subject include the following five cases:

[0190] The probability of poor ovarian response less than 5% is converted to an ovarian reserve score greater than 95 points;

[0191] The probability of poor ovarian response greater than or equal to 5% and less than 25% is converted to an ovarian reserve score greater than 75 points and less than or equal to 95 points;

[0192] The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted to an ovarian reserve score greater than 50 points and less than or equal to 75 points;

[0193] The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted to an ovarian reserve score greater than 25 points and less than or equal to 50 points;

[0194] The probability of poor ovarian response greater than or equal to 75% and less than 84% is converted to an ovarian reserve score greater than or equal to 16 points and less than or equal to 25 points.

[0195] In this application, the probability of low ovarian response is less than or equal to 5%: the ovarian reserve is graded A (ovarian reserve score ≥ 95 points). This reserve score indicates that the subject has a very sufficient ovarian reserve, indicating excellent fertility, suitable for natural pregnancy, and high flexibility in pregnancy planning. A good ovarian reserve helps maintain sex hormone balance and reduce the risk of cardiovascular disease and osteoporosis.

[0196] The probability of poor ovarian response is greater than 5% and less than or equal to 25%. The ovarian reserve is graded B (ovarian reserve score 75-<95 points). This score indicates that the subject's ovarian reserve is good and fertility remains high, but has begun to show a slight downward trend. Early pregnancy planning is recommended.

[0197] The probability of poor ovarian response is greater than 25% and less than or equal to 50%. This is classified as ovarian reserve grade C (ovarian reserve score 50-<75 points), indicating moderate ovarian reserve and decreased fertility. When the ovarian reserve drops to 50 points, menopause is likely around 10 years away. At this point, the rate of ovarian reserve decline begins to accelerate significantly, necessitating close attention to fertility planning. Initial decline in ovarian reserve may cause menstrual cycle changes and minor metabolic disturbances, such as weight gain or changes in fat distribution.

[0198] The probability of poor ovarian response is greater than 50% and less than or equal to 75%. This indicates that the ovarian reserve is graded D (ovarian reserve score 25-<50 points). This score indicates that the subject's ovarian reserve is low and the likelihood of natural pregnancy is significantly reduced. Assisted reproductive technology support is recommended as soon as possible. Decreased ovarian function may affect bone health, increase the risk of bone loss, and may cause mood swings and mild memory loss.

[0199] Probability of poor ovarian response greater than 75%: Ovarian reserve grade E (ovarian reserve score <25 points) indicates extremely low ovarian reserve, nearing fertility loss, and possibly approaching menopause. Particular attention should be paid to overall health, as ovarian dysfunction can lead to a significant decrease in estrogen levels, increasing the risk of atherosclerosis, osteoporosis, and sleep disorders.

[0200] When the system of the present application is used as software, a five-color management system for ovarian reserve assessment can be constructed, thereby displaying the above-mentioned ovarian reserve grades through different colors. For example, green can be used to represent grade A, light green to represent grade B, yellow to represent grade C, orange to represent grade D, and red to represent grade E. Different grades require different monitoring frequencies, for example, grade A: review once every 12 months or longer; grade B: review once every 6–12 months; grade C: review once every 6 months; grade D: review once every 3–6 months; grade E: ≤3 months or follow-up expert advice.

[0201] Based on the five-color management system, subjects should combine quantitative risks with marital partner status, do relevant work on fertility reserve, flexibly choose egg or embryo freezing, and achieve personalized fertility management of "early assessment, early intervention, and early retention", providing solid protection for individualized fertility and women's health management.

[0202] Ovarian endocrine age is an indicator that maps an individual's ovarian reserve score onto a population-based ovarian aging curve. Its purpose is to reflect the relative state of an individual's ovarian function, rather than their actual age. Specifically, by measuring anti-Mullerian hormone (AMH) levels and combining them with actual age, a statistical model is used to predict the probability of a poor response to ovarian stimulation. This probability is then normalized to the ovarian reserve score. Subsequently, an S-shaped ovarian aging curve constructed from large population data can be used to determine ovarian function trends across different age groups. The age on the curve corresponding to the probability of an individual's score is then found; this age is the ovarian endocrine age. If a young woman's ovarian reserve score corresponds to an endocrine age much higher than her actual age on the aging curve, this may indicate a more rapid decline in ovarian function and a potential premature decline in future fertility. Conversely, if her actual age is close to or below the average for her endocrine age, this indicates relatively ideal ovarian function.

[0203] In the module for calculating the subject's ovarian endocrine age, the subject's endocrine age is calculated using the subject's probability of low ovarian response based on any one of a two-parameter logistic regression model, a two-parameter Probit model, a three-parameter Gompertz model, or a Weibul curve.

[0204] Specifically, the present application generates a typical S-shaped curve based on any one of a two-parameter logistic regression model, a two-parameter Probit model, a three-parameter Gompertz model or a Weibull curve. By mapping the subject's standardized ovarian reserve score onto this S-shaped ovarian aging curve, the individual's ovarian endocrine age can be accurately calculated. This mapping process reflects the degree of match between the individual's ovarian reserve level and the ovarian aging trend of the entire population, thereby providing a strong basis for subsequent reproductive health assessment and individualized treatment plans. When constructing the ovarian aging curve, the inventors of the present application mapped the individual ovarian reserve score onto an S-shaped curve reflecting the aging trend of the population, and this mapping depends on setting a suitable threshold (i.e., the predicted probability of low ovarian response or ovarian reserve score) to determine whether there is a decrease in ovarian reserve (DOR). Through comparative analysis of four models: two-parameter logistic regression, two-parameter Probit, three-parameter Gompertz and Weibull growth curve, the inventors found that when the threshold was set to 0.15, various statistical indicators (such as AICc, BIC, SSE, MSE, RMSE and R²) were better than those set to 0.5. Among them, the improvement of R² value is particularly significant, and this threshold is highly consistent with the probability of occurrence of ovarian hyporesponsiveness in the actual population.

[0205] Specifically, using a cutoff of 0.15 significantly improved the fit of both the logistic regression model and other models. For example, the logistic regression model constructed based on the cubic term model to predict the probability of low ovarian response (ovarian reserve score) had an R² as high as 0.9911 at a cutoff of 0.15, and the Weibull model had an R² as high as 0.9930. When the cutoff was 0.5, the R²s were 0.9877 and 0.9873, respectively, indicating that the model with a cutoff of 0.15 was superior.

[0206] The threshold optimization process conducted in this application ensures that the model can more accurately capture subtle changes in future ovarian function when predicting the risk of low ovarian response and inferring ovarian endocrine age, thereby providing more reliable clinical decision-making basis. In this application, setting the threshold to 0.15 not only conforms to the principles of statistical optimization, but also is closer to clinical practice, with significant results.

[0207] In some embodiments, the endocrine age (age) is calculated based on the probability of ovarian hyporesponsiveness using the following formula 2, which is inversely deduced based on a two-parameter logistic curve. Endocrine ):

[0208] (Formula 2)

[0209] Where p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0210] Wherein, a1 is a fixed value, preferably 37.553; b1 is a fixed value, preferably 0.283.

[0211] In some embodiments, the standard error of endocrine age (SE age ) to calculate the endocrine age (age Endocrine ),

[0212]

[0213] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject; a2, b2 and c2 are unitless parameters;

[0214] Among them, a2 is a fixed value, the value is 0.283; b2 is a fixed value, the value is 0.012; c2 is a fixed value, the value is 0.168;

[0215] The lower and upper limits of the 95% confidence interval for calculating endocrine age are:

[0216] Calculate the standard error of endocrine age (se age ) to calculate the endocrine age (age Endocrine ),

[0217]

[0218] Wherein, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject; a2, b2 and c2 are unitless parameters;

[0219] Among them, a2 is a fixed value, the value is 0.283; b2 is a fixed value, the value is 0.012; c2 is a fixed value, the value is 0.168;

[0220] The lower and upper limits of the 95% confidence interval for calculating endocrine age are:

[0221] .

[0222] The significance of calculating the 95% confidence interval of the endocrine age in the present application is that firstly, the uncertainty of the estimation can be quantified. The ovarian endocrine age is a point estimate obtained by substituting the ovarian reserve score of the subject into the two-parameter logistic curve. However, the model is derived from a limited sample and the parameters are subject to errors. The 95% confidence interval (CI) takes these random errors into account: if the sample is repeatedly taken and the model is rebuilt under the same conditions, about 95% of the intervals obtained multiple times will cover the "true" endocrine age. It intuitively shows the accuracy of the estimated value through the interval width - the narrower the interval, the more confident the model is; the wider, the more cautious the interpretation should be. Secondly, it distinguishes statistically significant differences. By comparing the actual age of the subject with the 95% CI of the endocrine age, it can be determined whether the difference between the two is statistically significant. If the actual age falls outside the interval, it indicates that the ovarian function is significantly deviating from the average level of the same age group; if it falls within the interval, the difference may be caused by random errors. This helps to identify true "premature" or "delayed" ovarian aging in clinical practice. Thirdly, it supports individualized clinical decision-making. For women who need to develop intervention programs (such as whether to have children early or freeze eggs), the CI provides a range of confidence in risk assessment: narrow interval and higher than actual age → clear problem and urgent action; wide interval or across actual age → need to combine other indicators, retest or follow-up. This not only avoids making radical decisions due to excessive confidence, but also reduces the delay of opportunities due to excessive caution. Finally, it promotes risk communication and continuous optimization of the model. Reporting both the point estimate and the 95% CI can help subjects and medical staff directly see "results + confidence" and improve communication transparency; at the same time, it allows developers to monitor changes in interval width for different populations and different laboratory batches, so as to find potential deficiencies of the model in certain subgroups and iterate optimization. In short, the 95% confidence interval not only tells us "what is the predicted value", but also answers "how confident are we about this prediction". It is a key link to upgrade the system of the present application from a simple numerical output to an interpretable and reliable clinical tool.

[0223] In the module of predicting the age at which the ovarian reserve of the subject changes, the following formula three is used to calculate the time from the current ovarian reserve of the subject to the time when the ovarian reserve significantly decreases, leading to a significant decrease in fertility, i.e., the time when the probability of ovarian low response is 50%:

[0224] According to the endocrine age, the time to the age of 50 points of ovarian reserve (D1) is calculated, i.e., the difference between the age of p=0.5 obtained according to the logistic curve and the endocrine age;

[0225] (Formula three)

[0226] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0227] Preferably, a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine You can choose any value between the lower and upper limits of the 95% confidence interval of formula 2, and further optimize age Endocrine is the calculated value of Formula 2.

[0228] Furthermore, the age at which ovarian reserve reaches 50 points is calculated by the following formula: DOR ):

[0229] age DOR = age chronological + D1

[0230] Among them, age chronological represents the current age of the subject, and D1 is the D1 value calculated by formula 3.

[0231] In the module for predicting the age at which the subject's ovarian reserve changes, the following formula 4 is used to calculate the number of years from the subject's current ovarian reserve to the onset of perimenopause, that is, the number of years when the probability of ovarian low response is 95%:

[0232] Based on the endocrine age, the number of years until perimenopause (D2) was calculated, which was the difference between the age at which p was 0.95 obtained from the logistic curve and the endocrine age:

[0233] (Formula 4)

[0234] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0235] a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine Any value between the lower and upper limits of the 95% confidence interval of formula 2 can be selected to further Endocrine The calculated value of formula 2 is preferred.

[0236] Furthermore, the age at onset of perimenopause (age perimenopause) can be calculated using the following formula:

[0237] age perimenopause = agechronological + D2

[0238] Where agechronological represents the current age of the subject, and D2 is the D2 value calculated by formula 4.

[0239] The present application also provides a method for predicting ovarian aging in a subject, comprising:

[0240] a data collection step, which obtains data on the subject's age and anti-Mullerian hormone (AMH) level;

[0241] a step of calculating ovarian hyporesponsiveness function, which calculates the data obtained in the data collection step to calculate the subject's ovarian hyporesponsiveness probability (p), and further converts the ovarian hyporesponsiveness probability into a standardized ovarian reserve score;

[0242] a step of calculating the subject's endocrine age, which calculates the subject's endocrine age using the subject's probability of low ovarian response; and

[0243] The step of predicting the age at which the subject's ovarian reserve changes, using the subject's endocrine age to predict the year or age at which the subject will experience new changes in ovarian reserve.

[0244] Among them, the data collection steps, the steps of calculating the ovarian hyporesponsiveness function, the steps of calculating the subject's endocrine age, and the steps of predicting the age at which the subject's ovarian reserve changes involved in the method respectively correspond to the data collection system, the system for calculating the ovarian hyporesponsiveness function, the system for calculating the subject's endocrine age, and the system for predicting the age at which the subject's ovarian reserve changes involved in the above-mentioned system.

[0245] Example

[0246] Example 1

[0247] Controlled Ovarian Stimulation (COS) Therapy

[0248] Gn (i.e. human recombinant FSH) treatment was initiated on cycle day 2 or 3. The starting dose was chosen according to age, BMI (i.e. body mass index, a number obtained by dividing body weight in kilograms by the square of height in meters, which is currently an international standard for measuring the degree of obesity and health of the human body), menstrual 2-4 days FSH and AFC levels. During ovulation induction, the starting dose of Gn was adjusted according to ultrasound observation and serum E2 levels. GnRH antagonist treatment was initiated on stimulation day 5-7 when the diameter of the growing follicle was 10-12 mm. When at least 2 dominant follicles (diameter ≥ 18 mm) were visible by ultrasound, 5000-10000 IU of hCG was administered to trigger the final oocyte maturation. Oocyte retrieval was performed 36 hours after hCG administration. 1-3 embryos were transferred or embryo cryopreservation was performed. Luteal phase progesterone support was then provided.

[0249] In the embodiments of the present application, the subjects who received the above-mentioned GnRH antagonist treatment were received by the applicant of the present application between 2017-2021, and the serum AMH level, actual age and related clinical variables were recorded at baseline. To ensure data integrity, strict quality control measures were implemented to exclude records with missing values or extreme outliers. Finally, 16,327 antagonist cycles performed between January 2017 and December 2019 constituted the training cohort, while 15,596 cycles between January 2020 and December 2021 constituted an independent validation cohort for the construction of the system involved in the present application.

[0250] Sample collection and endocrine assays

[0251] For the subjects as described above, a venous blood sample was drawn and immediately inverted five times to promote complete blood clotting, serum was collected by centrifugation and used for endocrine evaluation. The serum AMH concentration of the subject was detected using a super-sensitive two-point ELISA kit (Ansh Labs, USA).

[0252] Among them, the AMH concentration of most subjects was detected on the second day of the menstrual cycle, and the testing time of the AMH concentration of a small number of subjects was adjusted according to actual needs.

[0253] The data situation of the system for constructing the model is shown in Table 1 below.

[0254] Table 1 Clinical and biochemical data of subjects treated with GnRH antagonist

[0255]

[0256] System model construction

[0257] In this example, the poor ovarian response of the above-mentioned subjects and the number of oocytes of the subjects less than 5 (specifically 0, 1, 2, 3 or 4) are defined as the result variable, and the age and AMH level are the prediction variables. Among them, the prediction model in this example is constructed using the data from 2017-2019, that is, the data of 16,327 subjects are used as the training set to preliminarily construct the model system of the present application, and the data from 2020-2021, that is, the data of 15,596 subjects are used as the validation set to verify the effect of the system model.

[0258] The probability of poor ovarian response is modeled using a logistic regression framework in order to make a direct comparison with the model previously published by the present applicant (Model-0, as disclosed in CN2021104387761). Three candidate specifications of age and AMH are evaluated:

[0259] Model-1 (continuous): age and AMH are included as untransformed continuous variables.

[0260] Model-2 (polynomial): age is represented by a quadratic term and AMH by a cubic term, based on exploratory generalized additive modeling.

[0261] The model discrimination ability is quantified by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and F1 score. Calibration is assessed by comparing the predicted probabilities with the observed POR rates through calibration plots. The net reclassification improvement (NRI) index is used to measure the incremental improvement compared with Model-0.

[0262] Subsequently, the relationship between the predicted proportion of poor ovarian response (also defined as predicted DOR in this study) and age is described. Given the expected sigmoidal pattern, a logistic growth curve is used to fit the age-DOR data.

[0263] All analyses were performed in JMP Pro 17.0 (SAS Institute) and R 4.4.1, with statistical significance defined as two-sided p<0.05.

[0264] Figure 1The calibration of Model-0, Model-1, Model-2, and the ideal model was compared on the training (2017-2019) and test (2020-2021) datasets. Model-2 performed best, with its curve closely aligned with the ideal model, especially in the low- and medium-probability ranges. Although Model-1 also performed well, Model-0 showed significant differences compared to the other models. The figure below zooms in on the 0-0.25 probability range, which is particularly important for the female population, which represents 82.3% of our dataset. In this zoomed-in view, Model-2 again outperforms the other models, demonstrating superior calibration, making it the most reliable model for predicting outcomes in this most common probability range.

[0265] Based on these findings, we selected Model-2 as the final updated version. Model calibration is crucial in clinical practice. Good calibration ensures that the predicted POR incidence accurately reflects the true incidence in the population, ensuring that each predicted value is close to the actual incidence. This accurate incidence estimate provides a solid foundation for subsequent predictions, such as the age at onset of perimenopause.

[0266] At the same time, the data of the above training group were used to fit the following formula for calculating the probability of ovarian hyporesponsiveness (p) of the subjects:

[0267]

[0268] (Formula 1)

[0269] As shown in Formula 1, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject, and e represents a natural constant;

[0270] wherein a is any value selected from -3.830 to -2.929, b is any value selected from 0.042 to 0.069, c is any value selected from 0.0017 to 0.0050, d is any value selected from -1.490 to -1.327, f is any value selected from 0.250 to 0.373, and g is any value selected from 0.091 to 0.130.

[0271] Ovarian aging curve and endocrine age determination

[0272] In this study, we aimed to further refine the ovarian aging curve by incorporating more data and exploring additional methods. First, we recognized that ovarian aging follows an S-shaped trajectory. Therefore, we applied a two-parameter logistic curve to describe the trend of the proportion of POR (i.e., p calculated by the above formula) predicted at different ages. POR was defined based on the newly developed Model-2, which applies a three-fold transformation to AMH. Using an actual POR incidence rate of 0.15 as the cutoff value for defining POR, we plotted the DOR proportion at different ages ( Figure 2 The logistic curve model achieved an improvement of r² of 0.991, which is better than the original 0.978, demonstrating a better overall fit.

[0273] Using the fitted S-shaped curve, we can map the predicted POR probability of any individual onto the ovarian aging curve to derive their "endocrine age." Specifically, by measuring AMH levels and recording the subject's actual age, we calculate the predicted POR probability. We then find the position corresponding to this probability on the S-shaped ovarian aging curve constructed from large-scale population data; the age at which the curve reaches this probability is defined as the ovarian endocrine age. If a young woman's ovarian reserve score corresponds to an endocrine age that is much higher than her actual age, this may indicate accelerated ovarian decline and a possible earlier decline in fertility. Conversely, if her endocrine age is close to or lower than her actual age, it indicates that her ovarian function is relatively well preserved. In this way, the ovarian endocrine age reflects the true state of ovarian reserve and more accurately reveals the extent and rate of ovarian aging than actual age.

[0274] According to the probability of low ovarian response, the endocrine age (age) can be calculated by reverse deduction based on the two-parameter logistic curve using the following formula 2. Endocrine ):

[0275] (Formula 2)

[0276] Where a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, and are unitless parameters; a1 is a fixed value, which is 37.553; b1 is a fixed value, which is 0.283.

[0277] Calculate the standard error of endocrine age (se age ) to calculate the endocrine age (age Endocrine ),

[0278]

[0279] Where p is the probability of ovarian low response of ovarian reserve function, a2, b2 and c2 are unitless parameters; a2 is a fixed value, which is 0.283; b2 is a fixed value, which is 0.012; c2 is a fixed value, which is 0.168;

[0280] The lower and upper limits of the 95% confidence interval for calculating endocrine age are:

[0281] .

[0282] Predicting the age of onset of future reproductive milestones

[0283] The "fixed interval hypothesis" posits that an individual's ovarian aging trajectory follows the same shape as the population average—meaning that the time intervals between stages of ovarian decline are constant, with only the starting points varying. In other words, although individuals may begin with varying levels of ovarian reserve, the overall pattern of functional decline over time can be described by a single S-shaped curve. Evidence supporting this hypothesis primarily comes from cross-sectional observations. This hypothesis provides both theoretical and methodological support for predicting future changes in ovarian reserve from a single measurement.

[0284] Based on this, we derived the time intervals required for an individual to reach a specific ovarian reserve status (predicted POR probability). First, the subject's current ovarian reserve score was mapped onto an S-shaped curve to determine her position in the overall aging process. Then, using the fitted curve, we calculated the intervals required to decrease from the current score to a predetermined POR probability (e.g., a specific POR probability corresponding to a specific ovarian reserve score) and converted these intervals to the age at which each milestone would be reached.

[0285] Specifically, the following formula 3 can be used to calculate the number of years from the subject's current ovarian reserve to the time when the ovarian reserve significantly declines, leading to a significant decline in fertility, that is, the number of years when the probability of ovarian low response is 50%:

[0286] Based on the endocrine age, the number of years until the age of ovarian reserve 50 points (D1) was calculated, which was the difference between the age with p = 0.5 obtained from the logistic curve and the endocrine age;

[0287] (Formula 3)

[0288] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters; a1 is 37.553; b1 can be any value between 0.260 and 0.301, and 0.283 is more preferred; age EndocrineYou can choose any value between the lower and upper limits of the 95% confidence interval of formula 2 to further optimize age Endocrine is the calculated value of Formula 2.

[0289] The age at which ovarian reserve reaches 50 points is calculated by the following formula (age DOR ):

[0290] age DOR = age chronological + D1

[0291] Among them, age chronological represents the current age of the subject, and D1 is the D1 value calculated by formula 3.

[0292] Furthermore, the following formula 4 can be used to calculate the number of years from the subject's current ovarian reserve to the onset of perimenopause, that is, the number of years when the probability of ovarian low response is 95%:

[0293] Based on the endocrine age, the number of years until perimenopause (D2) was calculated, which was the difference between the age at which p was 0.95 obtained from the logistic curve and the endocrine age:

[0294] (Formula 4)

[0295] p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of the inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters;

[0296] a1 can be any value between 37.224 and 37.883, and more preferably a1 is 37.553; b1 can be any value between 0.260 and 0.301, and more preferably b1 is 0.283; age Endocrine Any value between the lower and upper limits of the 95% confidence interval of formula 2 can be selected to further Endocrine The calculated value of formula 2 is preferred.

[0297] The age at onset of perimenopause (age perimenopause) can be calculated using the following formula:

[0298] age 围绝经 = age chronological + D2

[0299] Among them, age chronological represents the current age of the subject, and D2 is the D2 value calculated by formula 4.

[0300] Example 2 Selection of blood sampling time point

[0301] While most women have relatively stable AMH levels within a cycle, a proportion of women do show significant intra-cycle fluctuations. To assess the impact of these changes on predictive performance, we compared AMH measurements on cycle day 2, cycle day 6 and hCG trigger day (approximately cycle day 12) in GnRH antagonist cycles and constructed three different models using AMH and age to determine the extent to which the magnitude of AMH decline significantly impacted model accuracy.

[0302] Firstly, we performed normality transformation on non-normally distributed AMH values (log transformation) Figure 3 Figure 1A). Using linear mixed-effects models on log-transformed data, we found that AMH declined by approximately 17.4% and 49.7% (both p<0.0001; 12 Figure 3 Figure 1B) on cycle day 6 (AMH6) and cycle day 12 (AMH12) compared to cycle day 2 (AMH2), indicating that AMH levels peak in the early follicular phase and subsequently decline significantly.

[0303] ROC curve analysis revealed that the model incorporating cycle day 2 AMH and age achieved the highest AUC (0.868; 95% CI, 0.814-0.908). Although its AUC was not significantly different from the model incorporating cycle day 6 AMH and age (AUC=0.860; 95% CI, 0.805-0.902), it was significantly superior to the model incorporating cycle day 12 AMH and age (AUC=0.652; 95% CI, 0.584-0.715) Figure 3 Figure 1C). Furthermore, the confusion matrix metrics showed that the cycle day 2 AMH and age model had the lowest misclassification rate (0.1099) and was superior in terms of sensitivity, F1 score and Matthews Correlation Coefficient (MCC) to the other models, indicating that this model was more accurate and reliable in predicting POR in women with greater intra-cycle fluctuations in AMH.

[0304] Therefore, it is recommended that the model of the present application for predicting ovarian aging in a subject uses the AMH concentration at which it is the highest because at this time the AMH best reflects the status of ovarian reserve, i.e. is least affected by the negative regulation of E2.

[0305] Although the embodiments of the present application are described above, the present application is not limited to the specific embodiments and application fields described above, and the specific embodiments described above are merely illustrative and instructive, but not restrictive. A person of ordinary skill in the art can make many forms under the guidance of the present specification and without departing from the scope protected by the claims of the present application, and these all belong to the protection of the present application.​

Claims

1. A system for predicting ovarian aging in a subject, comprising: a data acquisition module for acquiring data on the age and anti-Mullerian hormone (AMH) level of the subject; A module for calculating ovarian hyporesponsiveness function, which is used to calculate the above data acquired in the data acquisition module, thereby calculating the subject's ovarian hyporesponsiveness probability (p), and further converting the ovarian hyporesponsiveness probability into a standardized ovarian reserve score; a module for calculating the subject's endocrine age, which calculates the subject's endocrine age using the subject's probability of low ovarian response; and The module predicts the age at which the ovarian reserve of the subject changes, which uses the subject's endocrine age to predict the year or age at which the subject will experience new changes in ovarian reserve.

2. The system according to claim 1, wherein: For women with regular menstruation, the anti-Mullerian hormone (AMH) level refers to the anti-Mullerian hormone concentration in the venous blood of the female subject on any day; For women with irregular menstruation, the anti-Mullerian hormone (AMH) level refers to the highest value of the anti-Mullerian hormone concentration in the venous blood of the female subject during multiple consecutive venous blood draws during the menstrual cycle.

3. The system according to claim 1, wherein: The poor ovarian response refers to the actual number of eggs retrieved by the subject being less than 5.

4. The system according to claim 1, wherein: In the module for calculating ovarian hyporesponsiveness, the probability of ovarian hyporesponsiveness (p) of the subject is calculated based on a logistic regression model using the subject's age and anti-Mullerian hormone (AMH) level.

5. The system according to claim 1, wherein In the module for calculating ovarian poor response function, the ovarian reserve score is calculated using the following conversion formula: Ovarian reserve score = (1 - probability of poor ovarian response) × 100.

6. The system according to claim 1, wherein: In the module for calculating the endocrine age of the subject, the subject's endocrine age is calculated using the subject's probability of low ovarian response based on any one of a two-parameter logistic regression model, a two-parameter Probit model, a three-parameter Gompertz model, or a Weibul curve.

7. The system according to claim 6, wherein: The threshold in the two-parameter logistic regression model, two-parameter Probit model, three-parameter Gompertz model, or Weibul curve was set at 0.

15.

8. The system according to claim 1, wherein: The calculated probability of poor ovarian response and standardized ovarian reserve score of the subjects include the following five situations: A low ovarian response probability of less than 5% is converted to an ovarian reserve score greater than 95 points; The probability of poor ovarian response greater than or equal to 5% and less than 25% is converted into an ovarian reserve score greater than 75 points and less than or equal to 95 points; The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted into an ovarian reserve score greater than 50 points and less than or equal to 75 points; The probability of poor ovarian response greater than or equal to 50% and less than 75% is converted into an ovarian reserve score greater than 25 points and less than or equal to 50 points; A probability of poor ovarian response greater than or equal to 75% and less than 84% is converted into an ovarian reserve score greater than or equal to 16 points and less than or equal to 25 points.

9. The system according to claim 4, wherein: In the module for calculating ovarian hyporesponsiveness, the probability (p) of the subject's ovarian hyporesponsiveness is calculated by performing a quadratic transformation on the subject's age and a cubic transformation on the subject's anti-Mullerian hormone (AMH) level data.

10. The system according to claim 9, wherein: Calculate the probability of poor ovarian response of the subjects ( p ) is the following formula 1: (Formula 1) in, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject, e represents a natural constant; Wherein, a, b, c, d, f, and g are unitless parameters; age represents the age of the subject, and AMH represents the anti-Mullerian hormone (AMH) level of the subject; In the module for calculating ovarian reserve function, the subject's age and the subject's anti-Mullerian hormone (AMH) level are taken as logarithms and substituted into Formula 1 for calculation.

11. The system according to claim 10, wherein: a is any value selected from -3.830 to -2.929, b is any value selected from 0.042 to 0.069, c is any value selected from 0.0017 to 0.0050, d is any value selected from -1.490 to -1.327, f is any value selected from 0.250 to 0.373, g is any value selected from 0.091 to 0.

130.

12. The system according to claim 5, wherein: In the module for calculating the endocrine age of the subject, according to the probability of ovarian low response, the endocrine age (age) is calculated by reverse deduction based on the two-parameter logistic curve using the following formula 2. Endocrine ): (Formula 2) in, p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of inflection point and growth rate parameters obtained based on the logistic curve, which are unitless parameters; Where a1 is a fixed value; b1 is a fixed value.

13. The system according to claim 12, wherein: In the module for calculating the subject's endocrine age, the standard error of the endocrine age is calculated ( se age ) to calculate the endocrine age (age Endocrine ), ； in, p is the calculated probability of ovarian low response used to characterize the current ovarian reserve function of the subject; a2, b2 and c2 are unitless parameters; Among them, a2 is a fixed value, the value is 0.283; b2 is a fixed value, the value is 0.012; c2 is a fixed value, the value is 0.168; The lower and upper limits of the 95% confidence interval for calculating endocrine age are: age Endocrine_lower = age Endocrine -1.96× se age age Endocrine_upper = age Endocrine +1.96× se age 。 14. The system according to claim 1, wherein: In the module for predicting the age at which the subject's ovarian reserve changes, the following formula 3 is used to calculate the number of years from the subject's current ovarian reserve to the time when the ovarian reserve significantly decreases, leading to a significant decline in fertility, that is, the number of years when the probability of ovarian hyporesponsiveness is 50%: According to the endocrine age, the number of years until the ovarian reserve reaches 50 points (D1) is calculated, that is, according to the logistic curve p =0.5 difference between age and endocrine age; (Formula 3) p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of inflectionPoint and growth rate parameters obtained based on the logistic curve, which are unitless parameters; a1 is any value between 37.224 and 37.883; b1 is any value between 0.260 and 0.301; age Endocrine It is any value between the lower and upper limits of the 95% confidence interval of Formula 2.

15. The system according to claim 14, wherein: The age at which ovarian reserve reaches 50 points is calculated by the following formula (age DOR ): age DOR = age chronological + D1 Among them, age chronological represents the current age of the subject, and D1 is the D1 value calculated by formula 3.

16. The system of claim 1, wherein: In the module for predicting the age at which the subject's ovarian reserve changes, the following formula 4 is used to calculate the number of years from the subject's current ovarian reserve to the onset of perimenopause, that is, the number of years when the probability of ovarian low response is 95%: According to the endocrine age, the number of years until perimenopause (D2) is calculated, that is, the number obtained according to the logistic curve. p The difference between age and endocrine age when α is 0.95: (Formula 4) p is the probability of low ovarian response of ovarian reserve function; a1 and b1 are the estimated values ​​of inflectionPoint and growth rate parameters obtained based on the logistic curve, which are unitless parameters; a1 is any value between 37.224 and 37.883; b1 is any value between 0.260 and 0.301; age Endocrine It is any value between the lower and upper limits of the 95% confidence interval of Formula 2.

17. The system according to claim 16, wherein: The age at onset of perimenopause was calculated using the following formula: 围绝经 ): age 围绝经 = age chronological + D2 Among them, age chronological represents the current age of the subject, and D2 is the D2 value calculated by formula 4.

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