Aiming at the complex correlation between fetal Y-chromosome concentration and pregnant women’s physiological indicators in non-invasive prenatal testing (NIPT), this paper, based on the testing data of pregnant women with high BMI, establishes a generalized additive model (GAM) to quantify the nonlinear relationship between fetal Y-chromosome concentration and pregnant women’s gestational age, BMI, and the number of uniquely mapped reads through data preprocessing, correlation analysis, model construction and validation. The results show that this model can explain 87.42% of the variation in Y-chromosome concentration, with a generalized cross-validation (GCV) score of 0.001572, indicating no obvious overfitting; the mean absolute error (MAE) is 0.0078 and the root mean square error (RMSE) is 0.0105, suggesting relatively high prediction accuracy. Gestational age, BMI, and the number of uniquely mapped reads all pass the highly significant test (P < 0.001), and their influence trends are consistent with clinical physiological laws. This study provides a scientific tool for the quantitative analysis of the variation law of fetal Y-chromosome concentration in NIPT testing and lays a foundation for the subsequent optimization of testing time points.

Fangyan Ma, Yanrui Liu. Construction and Solution of the Association Model for Fetal Y-Chromosome Concentration in Non-Invasive Prenatal Testing. Frontiers in Medical Science Research (2025), Vol. 7, Issue 6: 91-96. https://doi.org/10.25236/FMSR.2025.070611.

[1] Perinatal Medicine Branch, Chinese Medical Association. Expert Consensus on Non-Invasive Prenatal Genetic Testing (2022 Edition)[J]. Chinese Journal of Perinatal Medicine, 2022, 25(5): 321-327.

[2] Ashoor G, Al-Issa M, Al-Sannaa N, et al. The impact of maternal body mass index on cell-free fetal DNA fraction and non-invasive prenatal testing performance[J]. Prenatal Diagnosis, 2018, 38(1): 32-37.

[3] Du Meijie. Quantitative Study on Genotype and Haplotype and Non-Invasive Prenatal Testing [D]. Beijing: Tsinghua University, 2021.

[4] Smith G C, Pell J P, Dobbie R M. Maternal weight and the risk of adverse pregnancy outcome: a prospective study of 285 366 pregnancies[J]. BMJ, 2000, 320(7251): 1708-1712.

[5] Zhang Y, Liu C, Li J, et al. Influence of maternal BMI on the concentration of cell-free fetal DNA in maternal plasma and the performance of non-invasive prenatal testing[J]. Journal of Obstetrics and Gynaecology Research, 2019, 45(1): 168-174.

[6] Jiang Qiyuan, Xie Jinxin, Ye Jun. Mathematical Modeling [M]. Beijing: Higher Education Press, 2021.

[7] Hastie T, Tibshirani R. Generalized additive models[M]. London: Chapman & Hall, 1990.

[8] Xie Z H. Application of non-invasive prenatal testing in prenatal screening for fetal chromosomal diseases in assisted reproduction and natural pregnancy[D]. Lanzhou University, 2025.

[9] Wu H Y. Analysis of the detection performance of non-invasive prenatal testing in twin pregnant women[D]. Hebei North University, 2024.000337.

[10] Li S Q. Clinical application of NIPT in screening for fetal chromosomal abnormalities in IVF pregnancies[D]. Hebei Medical University, 2024.001829.