Abstract
Study question
Would it be possible to predict embryo ploidy by taking into account conventional morphological and morphokinetic parameters together with IL-6 concentration in spent culture medium?
Summary answer
Our artificial neural network (ANN) trained with blastocyst morphology, embryo morphokinetics and IL-6 concentration distinguished between euploid/aneuploid embryos in 65% of the testing dataset.
What is known already
The analysis of spent embryo culture media represents the protein and metabolic state of the embryo and could be a non-invasive method of obtaining information about embryo quality. The impact of the presence/absence of several proteins in embryo culture samples over clinical results has been widely studied. The IL-6 is one of the most mentioned protein for its effect on embryo development, implantation and likelihood of achieving a live birth. In this initial attempt, we examined the predictive value for euploidy of a model that took into account the concentration of IL-6 in the spent culture medium.
Study design, size, duration
This prospective study included 319 embryos with PGT-A results. Out of the total, 127 were euploid and 192 aneuploid embryos. Concentration of IL-6 in spent embryo culture media (collected on the day of trophectoderm biopsy-fifth/sixth day of development), morphokinetic parameters (division time to 2 cells-t2; to 3 cells-t3, to 4 cells-t4; to 5 cells-t5 and time of blastocyst formation-tB) and blastocyst morphological grade (according to ASEBIR criteria) were considered to predict the embryo ploidy.
Participants/materials, setting, methods
Embryos were cultured in EmbryoScope. The chromosome analysis was performed using next-generation sequence technology. The concentration of IL-6 was measured in 20µL of spent embryo culture media with ELISA kits. Morphokinetic parameters were automatically annotated and the blastocyst morphology was evaluated by senior embryologists based on blastocele expansion, inner cell mass and trophectoderm quality. All the embryos were divided into 70% for training, 15% for validating and 15% for testing our ANN model with MatLab®.
Main results and the role of chance
The general description for the euploid embryo population was the following: 2% of the embryos were graded as A, 71% were graded as B and 28% were graded as C; the means and standard deviations were 25.32±2.97 hours (h) for t2, 35.33±5.15h for t3, 37.30±5.43h for t4, 48.24±6.62h for t5 and 103.93±12.8h for tB; and the average of IL-6 concentration was 1.51±0.70 pg/ml. The general description for the aneuploid embryo population was the following: 1% of the embryos were graded as A, 48% were graded as B and 51% were graded as C; the means and standard deviations were 26.13±3.51h for t2, 36.70±4.29h for t3, 38.20±4.24h for t4, 49.86±6.89h for t5 and 107.10±8.29h for tB; and the average of IL-6 concentration was 1.47±0.71 pg/ml. Our ANN model showed a higher general success rate as we increased the variables considered in the final prediction of euploid embryos. The accuracy, sensitivity and specificity for the testing dataset were: 0.60, 0.12 and 0.87 with morphokinetic parameters; 0.63, 0.24 and 0.93 with morphokinetics and IL-6 concentration; and 0.65, 0.16 and 0.96 with morphokinetics, IL-6 concentration and blastocyst morphological grade.
Limitations, reasons for caution
The low sensitivity and high specificity achieved in our models indicated that they were more capable of detecting aneuploid than euploid embryos. As this was a preliminary study, the small number of embryos included in the test (n = 48) was also a limitation.
Wider implications of the findings
The results showed that our model tended to classify the embryos as aneuploid. More euploid embryos would be necessary to train our model and achieve better results in the prediction of chromosomally normal embryos. Further studies with large number of embryos and additional variables could improve the non-invasive ploidy prediction.
Trial registration number
not applicable
Growth Culture Conditions and Nutrient Signaling Modulating Yeast Chronological Longevity
