A. La Marca, M. Capuzzo, M. G. Imbrogno, V. Donno, G. Spedicato, S. Sacchi, M. Minasi, F. Spinella, P. Greco, F. Fiorentino, E. Greco
{"title":"女性年龄与胚胎整倍体的复杂关系。","authors":"A. La Marca, M. Capuzzo, M. G. Imbrogno, V. Donno, G. Spedicato, S. Sacchi, M. Minasi, F. Spinella, P. Greco, F. Fiorentino, E. Greco","doi":"10.23736/S0026-4784.20.04740-1","DOIUrl":null,"url":null,"abstract":"BACKGROUND\nFemale age is the strongest predictor of embryo chromosomal abnormalities and has a non linear relationship with the blastocyst euploidy rate: with advancing age there is an acceleration in the reduction of blastocyst euploidy. Aneuploidy was found to significantly increase with maternal age from 30% in embryos from young women to 70% in women older than 40 years old. The association seems mainly due to chromosomal abnormalities occurring in the oocyte.We aimed to elaborate a model for the blastocyst euploid rate for patients undergoing IVF/ICSI cycles using advanced machine learning techniques.\n\n\nMETHODS\nThis was a retrospective analysis of IVF/ICSI cycles performed from 2014 to 2016. In total, data of 3879 blastocysts were collected for the analysis. Patients underwent PGT-Aneuploidy analysis (PGT-A) at the Center for Reproductive Medicine of European Hospital, Rome, Italy have been included in the analysis. The method involved whole-genome amplification followed by array comparative genome hybridization. To model the rate of euploid blastocysts, the data were split into a train set (used to fit and calibrate the models) and a test set (used to assess models' predictive performance). Three different models were calibrated: a classical linear regression; a Gradient Boosted Tree (GBT) machine learning model; a model belonging to the Generalized Additive Models (GAM).\n\n\nRESULTS\nThe present study confirms that female age, which is the strongest predictor of embryo chromosomal abnormalities, and blastocyst euploidy rate have a non-linear relationship, well depicted by the GBT and the GAM models. According to this model, the rate of reduction in the percentage of euploid blastocysts increases with age: the yearly relative variation is -10% at the age of 37 and -30% at the age of 45. Other factors including male age, female and male body mass index, fertilization rate and ovarian reserve may only marginally impact on embryo euploidy rate.\n\n\nCONCLUSIONS\nFemale age is the strongest predictor of embryo chromosomal abnormalities and has a non-linear relationship with the blastocyst euploidy rate. Other factors related to both the male and female subjects may only minimally affect this outcome.","PeriodicalId":18745,"journal":{"name":"Minerva ginecologica","volume":" ","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"The complex relationship between female age and embryo euploidy.\",\"authors\":\"A. La Marca, M. Capuzzo, M. G. Imbrogno, V. Donno, G. Spedicato, S. Sacchi, M. Minasi, F. Spinella, P. Greco, F. Fiorentino, E. Greco\",\"doi\":\"10.23736/S0026-4784.20.04740-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND\\nFemale age is the strongest predictor of embryo chromosomal abnormalities and has a non linear relationship with the blastocyst euploidy rate: with advancing age there is an acceleration in the reduction of blastocyst euploidy. Aneuploidy was found to significantly increase with maternal age from 30% in embryos from young women to 70% in women older than 40 years old. The association seems mainly due to chromosomal abnormalities occurring in the oocyte.We aimed to elaborate a model for the blastocyst euploid rate for patients undergoing IVF/ICSI cycles using advanced machine learning techniques.\\n\\n\\nMETHODS\\nThis was a retrospective analysis of IVF/ICSI cycles performed from 2014 to 2016. In total, data of 3879 blastocysts were collected for the analysis. Patients underwent PGT-Aneuploidy analysis (PGT-A) at the Center for Reproductive Medicine of European Hospital, Rome, Italy have been included in the analysis. The method involved whole-genome amplification followed by array comparative genome hybridization. To model the rate of euploid blastocysts, the data were split into a train set (used to fit and calibrate the models) and a test set (used to assess models' predictive performance). Three different models were calibrated: a classical linear regression; a Gradient Boosted Tree (GBT) machine learning model; a model belonging to the Generalized Additive Models (GAM).\\n\\n\\nRESULTS\\nThe present study confirms that female age, which is the strongest predictor of embryo chromosomal abnormalities, and blastocyst euploidy rate have a non-linear relationship, well depicted by the GBT and the GAM models. According to this model, the rate of reduction in the percentage of euploid blastocysts increases with age: the yearly relative variation is -10% at the age of 37 and -30% at the age of 45. Other factors including male age, female and male body mass index, fertilization rate and ovarian reserve may only marginally impact on embryo euploidy rate.\\n\\n\\nCONCLUSIONS\\nFemale age is the strongest predictor of embryo chromosomal abnormalities and has a non-linear relationship with the blastocyst euploidy rate. Other factors related to both the male and female subjects may only minimally affect this outcome.\",\"PeriodicalId\":18745,\"journal\":{\"name\":\"Minerva ginecologica\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2020-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Minerva ginecologica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23736/S0026-4784.20.04740-1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minerva ginecologica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23736/S0026-4784.20.04740-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
The complex relationship between female age and embryo euploidy.
BACKGROUND
Female age is the strongest predictor of embryo chromosomal abnormalities and has a non linear relationship with the blastocyst euploidy rate: with advancing age there is an acceleration in the reduction of blastocyst euploidy. Aneuploidy was found to significantly increase with maternal age from 30% in embryos from young women to 70% in women older than 40 years old. The association seems mainly due to chromosomal abnormalities occurring in the oocyte.We aimed to elaborate a model for the blastocyst euploid rate for patients undergoing IVF/ICSI cycles using advanced machine learning techniques.
METHODS
This was a retrospective analysis of IVF/ICSI cycles performed from 2014 to 2016. In total, data of 3879 blastocysts were collected for the analysis. Patients underwent PGT-Aneuploidy analysis (PGT-A) at the Center for Reproductive Medicine of European Hospital, Rome, Italy have been included in the analysis. The method involved whole-genome amplification followed by array comparative genome hybridization. To model the rate of euploid blastocysts, the data were split into a train set (used to fit and calibrate the models) and a test set (used to assess models' predictive performance). Three different models were calibrated: a classical linear regression; a Gradient Boosted Tree (GBT) machine learning model; a model belonging to the Generalized Additive Models (GAM).
RESULTS
The present study confirms that female age, which is the strongest predictor of embryo chromosomal abnormalities, and blastocyst euploidy rate have a non-linear relationship, well depicted by the GBT and the GAM models. According to this model, the rate of reduction in the percentage of euploid blastocysts increases with age: the yearly relative variation is -10% at the age of 37 and -30% at the age of 45. Other factors including male age, female and male body mass index, fertilization rate and ovarian reserve may only marginally impact on embryo euploidy rate.
CONCLUSIONS
Female age is the strongest predictor of embryo chromosomal abnormalities and has a non-linear relationship with the blastocyst euploidy rate. Other factors related to both the male and female subjects may only minimally affect this outcome.
期刊介绍:
The journal Minerva Ginecologica publishes scientific papers on obstetrics and gynecology. Manuscripts may be submitted in the form of editorials, original articles, review articles, case reports, therapeutical notes, special articles and letters to the Editor. Manuscripts are expected to comply with the instructions to authors which conform to the Uniform Requirements for Manuscripts Submitted to Biomedical Editors by the International Committee of Medical Journal Editors (www.icmje.org). Articles not conforming to international standards will not be considered for acceptance.