The relationship between clinical-anamnestic data and cell-free fetal DNA level assessed by semiconductor sequencing within non-invasive prenatal testing

Introduction. Currently, non-invasive prenatal testing (NIPT) is widely used to assess a risk of fetal chromosomal anomalies. NIPТ accuracy depends on the cell-free fetal DNA (cffDNA) percentage relative to total cell-free DNA in the pregnant woman's blood (cfDNA fetal fraction, FF). Despite numerous studies, no consensus regarding FF-affecting factors has been reached yet. Aim: to investigate a relationship between FF and clinical-anamnestic parameters of pregnant women, pregnancy characteristics, and outcomes using the developed NIPТ technology. Materials and Methods. A prospective observational study was performed by assessing plasma samples from 5459 women with > 9 week-long singleton pregnancies. NIPТ was performed using semiconductor sequencing followed by bioinformatics data processing, including FF determination, according to a previously developed original algorithm. Results. Median FF was 11.7 [9.5–14.0] %. It was demonstrated that FF depends on blood collection tube type (p < 0.05). FF was found to decrease with woman age and body mass index, and increase with gestational age, elevated early prenatal screening (EPS) biochemical markers – pregnancy-associated plasma protein-A (РАРР-А) and free beta-subunit of human chorionic gonadotropin (β-hCG) levels (p < 0.05). It has been shown that the FF in pregnant women with trisomy 18 is lower than normal (p < 0.05). An increase in FF was observed in pregnant women with fetal congenital anomalies according to ultrasound results (p < 0.05). No association was found between FF and the conception type, first-trimester ultrasound parameters (nuchal translucency, crown-rump length, ultrasound chromosome anomalies markers), fetal trisomy 13 and 21, fetal sex chromosome anomalies, or pregnancy complications – preeclampsia, gestational diabetes, preterm birth, and fetal growth restriction (p > 0.05). Conclusion. The identified patterns are important to take into consideration while using and interpreting NIPТ.