| Literature DB >> 32215107 |
Tien-Truong Dang1, Thi-Mui Phung1, Hoang Le1,2, Thi-Bich-Van Nguyen3, Thi-Sim Nguyen4, Thi-Lien-Huong Nguyen3, Vu Thi Nga5, Dinh-Toi Chu6,7, Van-Luong Hoang1, Duy-Bac Nguyen1.
Abstract
BACKGROUND: Aneuploidy is a major cause of miscarriages and implantation failure. Preimplantation genetic testing for aneuploidy (PGT-A) by Next Generation Sequencing (NGS) is able to detect of the numeral and structural chromosomal abnormalities of embryos in vitro fertilization (IVF). AIM: This study was aimed to assess the relationship between maternal age and chromosomal abnormalities NGS technology.Entities:
Keywords: Aneuploidy; Blastocyst embryos; In vitro fertilization; Next-generation sequencing; Preimplantation genetic testing aneuploidy
Year: 2019 PMID: 32215107 PMCID: PMC7084032 DOI: 10.3889/oamjms.2019.875
Source DB: PubMed Journal: Open Access Maced J Med Sci ISSN: 1857-9655
PGS-NGS results in 578 embryos at blastocyst stage, (Pearson chi2(1) = 12.6506 pr = 0.000)
| Group 1 (<37 years old) | Group 2 (≥37 years old) | Total | P (Group 1 vs Group 2) | |
|---|---|---|---|---|
| Average age (Mean±SD | 32.06 ± 3.43 | 39.63 ± 2.96 | 34.26 ± 4.76 | 0.0000 |
| Normal embryos (n, %) | 254 61.95% | 77 45.83% | 331 57.27% | 0.0000 |
| Abnormal embryo (n, %) | 156 38.05% | 91 54.17% | 247 42.73% | 0.0000 |
| Total embryo (n, %) | 410 100% | 168 100% | 578 100% | |
Note: P values were determined by t test and Chi square test.
Figure 1Examples of chromosomal abnormalities detection by next-generation sequencing. Embryo number HU3 of patient N. T. H.: 42, XY, -2, -5, -6, -22, +4
Characteristics of aneuploidy
| Chrom-osome | Total chromosome tested | Aneuplody | Euploidy | ||
|---|---|---|---|---|---|
| n | % | n | % | ||
| 1 | 578 | 9 | 1.56% | 569 | 98.44% |
| 2 | 578 | 11 | 1.90% | 567 | 98.10% |
| 3 | 578 | 12 | 2.08% | 566 | 97.92% |
| 4 | 578 | 15 | 2.60% | 563 | 97.40% |
| 5 | 578 | 13 | 2.25% | 565 | 97.75% |
| 6 | 578 | 9 | 1.56% | 569 | 98.44% |
| 7 | 578 | 7 | 1.21% | 571 | 98.79% |
| 8 | 578 | 17 | 2.94% | 561 | 97.06% |
| 9 | 578 | 6 | 1.04% | 572 | 98.96% |
| 10 | 578 | 17 | 2.94% | 561 | 97.06% |
| 11 | 578 | 5 | 0.87% | 573 | 99.13% |
| 12 | 578 | 12 | 2.08% | 566 | 97.92% |
| 13 | 578 | 15 | 2.60% | 563 | 97.40% |
| 14 | 578 | 10 | 1.73% | 568 | 98.27% |
| 15 | 578 | 19 | 3.29% | 559 | 96.71% |
| 16 | 578 | 32 | 5.54% | 546 | 94.46% |
| 17 | 578 | 10 | 1.73% | 568 | 98.27% |
| 18 | 578 | 11 | 1.90% | 567 | 98.10% |
| 19 | 578 | 7 | 1.21% | 571 | 98.79% |
| 20 | 578 | 8 | 1.38% | 570 | 98.62% |
| 21 | 578 | 24 | 4.15% | 554 | 95.85% |
| 22 | 578 | 35 | 6.06% | 543 | 93.94% |
| XY | 578 | 18 | 3.11% | 560 | 96.89% |
| Total | 13294 | 322 | 2.42% | 12972 | 97.58% |
Figure 2Incidence of chromosomal abnormalities between two groups
Figure 3The linear regression line shows the correlation between maternal age and the rate of aneuploidy. R2: coefficient index