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女性年龄与胚胎整倍体之间的复杂关系。

The complex relationship between female age and embryo euploidy.

作者信息

La Marca Antonio, Capuzzo Martina, Imbrogno Maria G, Donno Valeria, Spedicato Giorgio A, Sacchi Sandro, Minasi Maria G, Spinella Francesca, Greco Pierfrancesco, Fiorentino Francesco, Greco Ermanno

机构信息

Department of Medical and Surgical Sciences of the Mother, Children and Adults, Polyclinic of Modena, Modena, Italy -

Department of Medical and Surgical Sciences of the Mother, Children and Adults, Polyclinic of Modena, Modena, Italy.

出版信息

Minerva Obstet Gynecol. 2021 Feb;73(1):103-110. doi: 10.23736/S2724-606X.20.04740-1. Epub 2020 Dec 11.

Abstract

BACKGROUND

Female age is the strongest predictor of embryo chromosomal abnormalities and has a nonlinear 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 in-vitro fertilization/intra cytoplasmic sperm injection (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 nonlinear 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.

摘要

背景

女性年龄是胚胎染色体异常的最强预测指标,且与囊胚整倍体率呈非线性关系:随着年龄增长,囊胚整倍体率下降加速。研究发现,非整倍体率随母亲年龄显著增加,从年轻女性胚胎中的30%增至40岁以上女性胚胎中的70%。这种关联似乎主要归因于卵母细胞中发生的染色体异常。我们旨在利用先进的机器学习技术,为接受体外受精/卵胞浆内单精子注射(IVF/ICSI)周期的患者建立一个囊胚整倍体率模型。

方法

这是一项对2014年至2016年进行的IVF/ICSI周期的回顾性分析。总共收集了3879个囊胚的数据用于分析。纳入分析的患者在欧洲医院(意大利罗马)生殖医学中心接受了植入前基因检测-非整倍体分析(PGT-A)。该方法包括全基因组扩增,随后进行阵列比较基因组杂交。为了建立整倍体囊胚率模型,数据被分为训练集(用于拟合和校准模型)和测试集(用于评估模型的预测性能)。校准了三种不同的模型:经典线性回归;梯度提升树(GBT)机器学习模型;属于广义相加模型(GAM)的模型。

结果

本研究证实,女性年龄作为胚胎染色体异常的最强预测指标,与囊胚整倍体率呈非线性关系,GBT和GAM模型能很好地描述这种关系。根据该模型,整倍体囊胚百分比的下降率随年龄增加:37岁时每年的相对变化为-10%,45岁时为-30%。其他因素,包括男性年龄、女性和男性体重指数、受精率和卵巢储备,对胚胎整倍体率的影响可能微乎其微。

结论

女性年龄是胚胎染色体异常的最强预测指标,与囊胚整倍体率呈非线性关系。与男性和女性相关的其他因素可能对这一结果影响极小。

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