Abstract：
Can a set of metabolites present in embryo culture media correlate with embryo implantation? Case-control study in two phases: discovery phase (101 samples) and validation phase (169 samples), collected between 2018 and 2022, with a total of 218 participants. Culture media samples with known implantation outcomes were collected after blastocyst embryo transfer (including both PGT and non-PGT cycles) and were analyzed using chromatography followed by mass spectrometry. The spectra were processed and analyzed using statistical and machine learning techniques to identify biomarkers associated with embryo implantation, and to develop a predictive model. In the discovery phase, 148 embryo implantation biomarkers were identified using high resolution equipment, and 47 of them were characterized. Our results indicate a significant enrichment of tryptophan metabolism, arginine and proline metabolism, and lysine degradation biochemical pathways. After transferring the method to a lower resolution equipment, a model able to assign a Metabolite Pregnancy Index (MPI) to each embryo culture media was developed, taking the concentration of 36 biomarkers as input. Applying this model to 20% of the validation samples (N=34) used as the test set, an accuracy of 85.29% was achieved, with a PPV (Positive Predictive Value) of 88% and a NPV (Negative Predictive Value) of 77.78%. Additionally, informative results were obtained for all the analyzed samples. Metabolite concentration in the media after in vitro culture shows correlation with embryo implantation potential. Furthermore, the mathematical combination of biomarker concentrations using Artificial Intelligence techniques can be used to predict embryo implantation outcome with an accuracy of around 85%.
摘要：
胚胎培养基中存在的一组代谢物与胚胎植入相关吗？病例对照研究分为两个阶段：发现阶段（101个样品）和验证阶段（169个样品），在2018年至2022年之间收集，共有218名参与者。在胚泡胚胎移植(包括PGT和非PGT周期)后收集具有已知植入结果的培养基样品,并使用色谱法随后通过质谱法进行分析。使用统计和机器学习技术对光谱进行处理和分析，以识别与胚胎植入相关的生物标志物。并建立预测模型。在发现阶段,148胚胎植入生物标志物使用高分辨率设备进行鉴定，其中47个是特征。我们的结果表明色氨酸代谢显着富集，精氨酸和脯氨酸代谢，和赖氨酸降解的生化途径。将该方法转移到分辨率较低的设备后，开发了一种能够为每种胚胎培养基分配代谢物妊娠指数（MPI）的模型，以36种生物标志物的浓度作为输入。将该模型应用于20%的验证样本(N=34)作为测试集，达到85.29%的准确度，PPV（正预测值）为88%，NPV（负预测值）为77.78%。此外,所有分析样品均获得了翔实的结果。体外培养后培养基中的代谢物浓度与胚胎植入潜力相关。此外，使用人工智能技术的生物标志物浓度的数学组合可用于预测胚胎植入结果,准确率约为85%.
