Blastocyst telomere length predicts successful implantation after frozen-thawed embryo transfer

Do embryos with longer telomere length (TL) at the blastocyst stage have a higher capacity to survive after frozen-thawed embryo transfer (FET)?

Digitally estimated TL using low-pass whole genome sequencing (WGS) data from the preimplantation genetic testing for aneuploidy (PGT-A) process demonstrates that blastocyst TL is the most essential factor associated with likelihood of implantation.

The lifetime TL is established in the early cleavage cycles following fertilization through a recombination-based lengthening mechanism and starts erosion beyond the blastocyst stage. In addition, a telomerase-mediated slow erosion of TL in human fetuses has been observed from a gestational age of 6-11 weeks. Finally, an abnormal shortening of telomeres is likely involved in embryo loss during early development.

Blastocyst samples were obtained from patients who underwent PGT-A and FET in an IVF center from March 2015 to May 2018. Digitally estimated mitochondrial copy number (mtCN) and TL were used to study associations with the implantation potential of each embryo.

In total, 965 blastocysts from 232 cycles (164 patients) were available to investigate the biological and clinical relevance of TL. A WGS-based workflow was applied to determine the ploidy of each embryo. Data from low-pass WGS-PGT-A were used to estimate the mtCN and TL for each embryo. Single-variant and multi-variant logistic regression, decision tree, and random forest models were applied to study various factors in association with the implantation potential of each embryo.

Of the 965 blastocysts originally available, only 216 underwent FET. While mtCN from the transferred embryos is significantly associated with the ploidy call of each embryo, mtCN has no role in impacting IVF outcomes after an embryo transfer in these women. The results indicate that mtCN is a marker of embryo aneuploidy. On the other hand, digitally estimated TL is the most prominent univariant factor and showed a significant positive association with pregnancy outcomes (P < 0.01, odds ratio 79.1). We combined several maternal and embryo parameters to study the joint effects on successful implantation. The machine learning models, namely decision tree and random forest, were trained and yielded classification accuracy of 0.82 and 0.91, respectively. Taken together, these results support the vital role of TL in governing implantation potential, perhaps through the ability to control embryo survival after transfer.

The small sample size limits our study as only 216 blastocysts were transferred. The number was further reduced to 153 blastocysts, where pregnancy outcomes could be accurately traced. The other limitation of this study is that all data were collected from a single IVF center. The uniform and controlled operation of IVF cycles in a single center may cause selection bias.

We present novel findings to show that digitally estimated TL at the blastocyst stage is a predictor of pregnancy capacity after a FET cycle. As elective single-embryo transfer has become the mainstream direction in reproductive medicine, prioritizing embryos based on their implantation potential is crucial for clinical infertility treatment in order to reduce twin pregnancy rate and the time to pregnancy in an IVF center. The AI-powered, random forest prediction model established in this study thus provides a way to improve clinical practice and optimize the chances for people with fertility problems to achieve parenthood.

This study was supported by a grant from the National Science and Technology Council, Taiwan (MOST 108-2321-B-006-013 -). There were no competing interests.

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