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Minerva Ginecologica 2020 Dec 11

DOI: 10.23736/S0026-4784.20.04740-1


language: English

The complex relationship between female age and embryo euploidy

Antonio LA MARCA 1 , Martina CAPUZZO 1, Maria G. IMBROGNO 1, Valeria DONNO 1, Giorgio A. SPEDICATO 2, Sandro SACCHI 1, Maria Giulia MINASI 3, Francesca SPINELLA 4, Pierfrancesco GRECO 3, Francesco FIORENTINO 4, Ermanno GRECO 3, 5

1 Department of Medical and Surgical Sciences of the Mother, Children and Adults, Policlinico di Modena, Modena, Italy; 2 UnipolSai Assicurazioni SpA, Bologna, Italy; 3 Centre for Reproductive Medicine, Villa Mafalda, Rome, Italy; 4 GENOMA Group, Molecular Genetics Laboratories, Rome, Italy; 5 UniCamillus, Rome, Italy


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.

KEY WORDS: Female age; Euploidy rate; Predictive model; Machine learning; PGT-A; IVF/ICSI

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