Overture Life, 28108, Alcobendas, Madrid, Spain.
Texas A&M University Corpus Christi, Corpus Christi, Texas, 78412, USA.
Reprod Sci. 2024 Sep;31(9):2706-2717. doi: 10.1007/s43032-024-01583-y. Epub 2024 Jun 4.
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%.
胚胎培养液中的一组代谢产物能否与胚胎着床相关？本研究采用病例对照设计，分两阶段进行：发现阶段（101 例样本）和验证阶段（169 例样本），共纳入 218 例患者，样本收集时间为 2018 年至 2022 年。胚胎培养液来自囊胚期胚胎移植后（包括 PGT 和非 PGT 周期），采用色谱-质谱联用技术进行分析。通过统计学和机器学习技术对图谱进行处理和分析，以确定与胚胎着床相关的生物标志物，并建立预测模型。在发现阶段，使用高分辨率设备鉴定了 148 个胚胎着床相关的生物标志物，其中 47 个得到了验证。研究结果表明，色氨酸代谢、精氨酸和脯氨酸代谢以及赖氨酸降解等生化途径显著富集。在将该方法转移到低分辨率设备上后，建立了一个能够为每个胚胎培养液分配代谢产物妊娠指数（MPI）的模型，该模型以 36 种生物标志物的浓度为输入。将该模型应用于验证阶段的 20%（N=34）的样本作为测试集，模型的准确率为 85.29%，阳性预测值为 88%，阴性预测值为 77.78%。此外，对所有分析样本均获得了有意义的结果。体外培养后胚胎培养液中的代谢物浓度与胚胎着床潜能相关。此外，使用人工智能技术对生物标志物浓度进行数学组合可用于预测胚胎着床结局，准确率约为 85%。
