OvaRePred: Online tool for predicting the age of fertility milestones.

Ovarian aging is believed to follow a growth curve model. 4 Furthermore, the fixed interval hypothesis proposes that the time spans between different reproductive milestones, such as subfertility, sterility, irregularity, and menopause, are fixed in the general population (as shown in Figure 1 A). 4 Building on this hypothesis, our objective was to establish a growth curve that illustrates ovarian aging using the cross-sectional data of 21,219 cycles from 2017 to 2018 obtained from Peking University Third Hospital. 5 All cycles were included in the analysis with no exclusions. By combining information about an individual’s current ovarian reserve status with the ovarian aging curve, the time required to reach a specific ovarian reserve status can be calculated. Figure 1 Ovarian aging curve (A) Fixed interval hypothesis: A cross-sectional study conducted a century ago, concluded that the time intervals between different fertility states were relatively fixed when no birth control strategies are used. 4 The study also provided characteristic curves of different fertility milestones. (B) Ovarian aging curve for all women undergoing ovarian stimulation. (C) Ovarian aging curve for individuals with or without ovulatory disorders. (D) Ovarian aging curve for individuals with or without uterine factor infertility. Ovarian aging curve (A) Fixed interval hypothesis: A cross-sectional study conducted a century ago, concluded that the time intervals between different fertility states were relatively fixed when no birth control strategies are used. 4 The study also provided characteristic curves of different fertility milestones. (B) Ovarian aging curve for all women undergoing ovarian stimulation. (C) Ovarian aging curve for individuals with or without ovulatory disorders. (D) Ovarian aging curve for individuals with or without uterine factor infertility. To establish the ovarian aging curve, we used various growth curve models to investigate the relationship between the proportion of DOR and age. Ultimately, we selected a logistic growth curve model with a coefficient of determination ( r 2 ) of 0.978, indicating that it can explain 97.8% of the relationship between DOR proportion and age. In this study, DOR was defined based on the AAFA model. 1 The growth curve depicting the relationship between DOR proportion and age is shown in Figure 1 B. Notably, this curve has similar characteristics to the growth curve model of the fixed interval hypothesis constructed a century ago, which depicted changes of fertility milestones over age in a cohort that did not use birth control methods ( Figure 1 A). Diseases or conditions that affect ovarian function can impact the ovarian aging curve. Therefore, we generated specific ovarian aging curves for different diseases or conditions, including endometriosis, ovulatory disorders, uterine factors, fallopian tube factors, and body mass index (BMI). Infertility factors were categorized into two groups (present or absent), and BMI was classified into three groups with cutoff values of 24 kg/m 2 and 28 kg/m 2 ; however, none of the infertility factors or BMI categories had a statistically significant effect on the growth curves. The growth curves for ovulatory disorders (p value of 0.189) and uterine factor infertility (p value of 0.989) are shown in Figures 1 C and 1D, respectively. The lack of statistical differences in the ovarian aging curves among different diseases or BMI categories may be attributed to the lack of data about the severity of the diseases and their comorbidities. To address this limitation, it is crucial to conduct follow-up studies using standardized data that document the severity of different diseases and their associated comorbidities. We selected the growth curve in Figure 1 B, which represents the entire population, as the final ovarian aging curve for our prediction model. In our study, the population with DOR corresponds to the predicted population with POR. The DOR ratio presented in the ovarian aging curve ( Figure 1 B) can be considered as the probability of DOR and, consequently, the probability of POR. This ratio also corresponds to the ovarian reserve score in the AA, AFA, and AAFA models used to assess ovarian reserve. 1 , 2 , 3 For instance, by using the current ovarian reserve scores and the ovarian aging curve, the time interval between the current ovarian reserve score and a future ovarian reserve score can be predicted. Thus, the comprehensive tool can be used to determine the predicted time interval for DOR, indicated by a DOR proportion of 50%, as well as for perimenopause, indicated by a DOR proportion of 94.0%, which represents the lowest ovarian reserve score in the data assessed by the AAFA model. With knowledge of the current age and the time interval, the age at which a future fertility milestone is likely to occur can easily be determined. The absence of a postmenopausal population in our dataset meant that the comprehensive tool could only predict perimenopause, which aligns with the population in our reproductive center that had the poorest ovarian reserve. DOR typically occurs approximately 10 years before menopause, and our ovarian aging curve reflects a similar pattern with DOR corresponding approximately to the sterility population represented in Figure 1 A. We developed the comprehensive tool into an online tool called OvaRePred for evaluating the current ovarian reserve status as well as predicting the future age at which DOR and perimenopause may occur. OvaRePred can be accessed at http://121.43.113.123:9999/ . Finally, individuals with diseases or conditions that could potentially affect AMH levels are advised to exercise caution when using OvaRePred or to avoid using it altogether because they can lead to inaccurate ovarian reserve scores for certain individuals. Factors that can contribute to variations in AMH levels include irregular menstrual cycles, diseases that impact ovarian function, smoking, exposure to environmental pollutants, and/or use of oral contraceptives. Specifically, when AMH levels fluctuate in the range of 3 ng/mL, the algorithms of our ovarian reserve models may generate significant fluctuations in the ovarian reserve score for an individual.

This technology has applied for Chinese invention patents (No. 202111509608.3 and 202210886125.3) and PCT patents (No. PCT/CN2022/078998 and PCT/CN2022/111753).

OvaRePred will allow reproductive-aged women to make their own childbirth plan according to their own ovarian reserve status and may help them manage other diseases by taking their current and future ovarian reserve status into consideration.