Identification of common genetic polymorphisms associated with down-regulated gonadotropin levels in an exome-wide association study
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GDF9His209GlnfsTer6/S428T and GDF9Q321X/S428T bi-allelic variants caused female subfertility with defective follicle enlargement
Background Antral follicles consist of an oocyte cumulus complex surrounding by somatic cells, including mural granulosa cells as the inner layer and theca cells as the outsider layer. The communications between oocytes and granulosa cells have been extensively explored in in vitro studies, however, the role of oocyte-derived factor GDF9 on in vivo antral follicle development remains elusive due to lack of an appropriate animal model. Clinically, the phenotype of GDF9 variants needs to be determined. Methods Whole-exome sequencing (WES) was performed on two unrelated infertile women characterized by an early rise of estradiol level and defect in follicle enlargement. Besides, WES data on 1,039 women undergoing ART treatment were collected. A Gdf9Q308X/S415T mouse model was generated based on the variant found in one of the patients. Results Two probands with bi-allelic GDF9 variants (GDF9His209GlnfsTer6/S428T, GDF9Q321X/S428T) and eight GDF9S428T heterozygotes with normal ovarian response were identified. In vitro experiments confirmed that these variants caused reduction of GDF9 secretion, and/or alleviation in BMP15 binding. Gdf9Q308X/S415T mouse model was constructed, which recapitulated the phenotypes in probands with abnormal estrogen secretion and defected follicle enlargement. Further experiments in mouse model showed an earlier expression of STAR in small antral follicles and decreased proliferative capacity in large antral follicles. In addition, RNA sequencing of granulosa cells revealed the transcriptomic profiles related to defective follicle enlargement in the Gdf9Q308X/S415T group. One of the downregulated genes, P4HA2 (a collagen related gene), was found to be stimulated by GDF9 protein, which partly explained the phenotype of defective follicle enlargement. Conclusions GDF9 bi-allelic variants contributed to the defect in antral follicle development. Oocyte itself participated in the regulation of follicle development through GDF9 paracrine effect, highlighting the essential role of oocyte-derived factors on ovarian response.
GDF9His209GlnfsTer6/S428T and GDF9Q321X/S428T bi-allelic variants caused female subfertility with defective follicle enlargement
Background Antral follicles consist of an oocyte cumulus complex surrounding by somatic cells, including mural granulosa cells as the inner layer and theca cells as the outsider layer. The communications between oocytes and granulosa cells have been extensively explored in in vitro studies, however, the role of oocyte-derived factor GDF9 on in vivo antral follicle development remains elusive due to lack of an appropriate animal model. Clinically, the phenotype of GDF9 variants needs to be determined. Methods Whole-exome sequencing (WES) was performed on two unrelated infertile women characterized by an early rise of estradiol level and defect in follicle enlargement. Besides, WES data on 1,039 women undergoing ART treatment were collected. A Gdf9Q308X/S415T mouse model was generated based on the variant found in one of the patients. Results Two probands with bi-allelic GDF9 variants (GDF9His209GlnfsTer6/S428T, GDF9Q321X/S428T) and eight GDF9S428T heterozygotes with normal ovarian response were identified. In vitro experiments confirmed that these variants caused reduction of GDF9 secretion, and/or alleviation in BMP15 binding. Gdf9Q308X/S415T mouse model was constructed, which recapitulated the phenotypes in probands with abnormal estrogen secretion and defected follicle enlargement. Further experiments in mouse model showed an earlier expression of STAR in small antral follicles and decreased proliferative capacity in large antral follicles. In addition, RNA sequencing of granulosa cells revealed the transcriptomic profiles related to defective follicle enlargement in the Gdf9Q308X/S415T group. One of the downregulated genes, P4HA2 (a collagen related gene), was found to be stimulated by GDF9 protein, which partly explained the phenotype of defective follicle enlargement. Conclusions GDF9 bi-allelic variants contributed to the defect in antral follicle development. Oocyte itself participated in the regulation of follicle development through GDF9 paracrine effect, highlighting the essential role of oocyte-derived factors on ovarian response.
Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review
Background In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. Objective The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. Methods A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. Results Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. Conclusions These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.
Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review (Preprint)
BACKGROUND In the realm of in vitro fertilization (IVF), artificial intelligence (AI) models serve as invaluable tools for clinicians, offering predictive insights into ovarian stimulation outcomes. Predicting and understanding a patient’s response to ovarian stimulation can help in personalizing doses of drugs, preventing adverse outcomes (eg, hyperstimulation), and improving the likelihood of successful fertilization and pregnancy. Given the pivotal role of accurate predictions in IVF procedures, it becomes important to investigate the landscape of AI models that are being used to predict the outcomes of ovarian stimulation. OBJECTIVE The objective of this review is to comprehensively examine the literature to explore the characteristics of AI models used for predicting ovarian stimulation outcomes in the context of IVF. METHODS A total of 6 electronic databases were searched for peer-reviewed literature published before August 2023, using the concepts of IVF and AI, along with their related terms. Records were independently screened by 2 reviewers against the eligibility criteria. The extracted data were then consolidated and presented through narrative synthesis. RESULTS Upon reviewing 1348 articles, 30 met the predetermined inclusion criteria. The literature primarily focused on the number of oocytes retrieved as the main predicted outcome. Microscopy images stood out as the primary ground truth reference. The reviewed studies also highlighted that the most frequently adopted stimulation protocol was the gonadotropin-releasing hormone (GnRH) antagonist. In terms of using trigger medication, human chorionic gonadotropin (hCG) was the most commonly selected option. Among the machine learning techniques, the favored choice was the support vector machine. As for the validation of AI algorithms, the hold-out cross-validation method was the most prevalent. The area under the curve was highlighted as the primary evaluation metric. The literature exhibited a wide variation in the number of features used for AI algorithm development, ranging from 2 to 28,054 features. Data were mostly sourced from patient demographics, followed by laboratory data, specifically hormonal levels. Notably, the vast majority of studies were restricted to a single infertility clinic and exclusively relied on nonpublic data sets. CONCLUSIONS These insights highlight an urgent need to diversify data sources and explore varied AI techniques for improved prediction accuracy and generalizability of AI models for the prediction of ovarian stimulation outcomes. Future research should prioritize multiclinic collaborations and consider leveraging public data sets, aiming for more precise AI-driven predictions that ultimately boost patient care and IVF success rates.
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