This study, based on NIPT data from 1082 male fetuses, systematically analyzed the relationship between fetal Y chromosome concentration and factors such as gestational age and maternal BMI, and constructed a model to predict the optimal testing time. Results showed that Y chromosome concentration was positively correlated with gestational age (r=0.456, p<0.001) and negatively correlated with BMI (r=-0.234, p=0.002), suggesting that concentration increases with gestational age, while a higher BMI delays the achievement of effective concentrations. To address multicollinearity and outliers, a robust Bayesian ridge regression model was employed, and further comparisons were made with multiple linear regression, nonlinear models, and random forest methods. The random forest model demonstrated the best prediction performance (R²=0.9924, MAE=0.115 weeks), enabling individualized NIPT timing prediction with subweekly accuracy. This proposed model provides a scientific basis for optimizing NIPT testing time, reducing duplicate sampling, and improving test accuracy in clinical practice.

Panlin Li, Ning Zhang. Prediction of Optimal NIPT Timing and Analysis of Influencing Factors of Fetal Y Chromosome Concentration Based on Robust Bayesian Ridge Regression. Academic Journal of Computing & Information Science (2025), Vol. 8, Issue 10: 99-107. https://doi.org/10.25236/AJCIS.2025.081013.

[1] Deng C, Zhang Y, Luo H, et al. Factors affecting the fetal fraction in noninvasive prenatal screening: a review[J]. Frontiers in Pediatrics, 2022, 10: 895142.

[2] Mousavi S, Shokri Z, Bastani P, et al. Factors affecting low fetal fraction in fetal screening with cell-free DNA in pregnant women: a systematic review and meta-analysis[J]. BMC Pregnancy and Childbirth, 2022, 22(1): 918.

[3] Ju J, Li R, Wu Y, et al. Estimation of cell-free fetal DNA fraction from maternal plasma based on linkage disequilibrium information[J]. NPJ Genomic Medicine, 2021, 6: 26.

[4] Scheffer P G, van der Meij K R M, van de Kamp J M, et al. Association between low fetal fraction in cell-free DNA and aneuploidy: clinical implications[J]. Prenatal Diagnosis, 2021, 41(6): 707-715.

[5] Shree R, Sabol B A, Krantz D A, et al. Low fetal fraction in obese women at first trimester cell-free DNA testing[J]. Prenatal Diagnosis, 2021, 41(1): 25-31.

[6] Becking E C, Moison A M, Zwarthoff E C, et al. Fetal fraction of cell-free DNA and its clinical significance[J]. Clinical Chemistry, 2020, 66(4): 567-574.

[7] Lee K H, Park J H, Jung Y M, et al. Impact of maternal obesity on fetal fraction during NIPT[J]. Prenatal Diagnosis, 2020, 40(7): 836-843.

[8] Salama S A, Ali M, Elsaid R, et al. Effect of gestational age on fetal fraction in maternal plasma during NIPT[J]. Journal of Obstetrics and Gynaecology, 2021, 41(3): 317-322.

[9] Sun Y, Ma D, Wang Y, et al. Maternal factors influencing fetal fraction in noninvasive prenatal testing[J]. European Journal of Obstetrics & Gynecology and Reproductive Biology, 2020, 252: 151-157.

[10] Wu H, Zhang J, Wang X, et al. Predicting low fetal fraction in early gestation for noninvasive prenatal testing[J]. BMC Genomics, 2021, 22(1): 52.

[11] García-Belenguer S, López-Fernández H, Gómez-Martín C, et al. Machine learning models for fetal fraction prediction in non-invasive prenatal testing[J]. Bioinformatics, 2021, 37(12): 1886-1892.

[12] Zhang J, Chen Y, Wang L, et al. A meta-analysis of maternal BMI and its effect on fetal fraction and NIPT accuracy[J]. Clinical Chemistry, 2022, 68(1): 53-60.

[13] Li Y, Zhou Q, Zhou L, et al. Fetal fraction variation in maternal plasma as a predictor for trisomy 21 screening performance[J]. BMC Pregnancy and Childbirth, 2021, 21(1): 375.

[14] Qian Y, Chen S, Yang X, et al. Optimization of fetal fraction prediction models for improving NIPT accuracy[J]. Journal of Molecular Diagnostics, 2022, 24(4): 493-501.

[15] Patel S, Gupta A, Sharma R, et al. Investigating the relationship between BMI and fetal fraction in NIPT[J]. Journal of Clinical Medicine, 2023, 12(2): 504.