ArticleFrontiers in cell and developmental biology2026
Correlation between novel hematological parameters and assisted pregnancy outcome in frozen-thawed embryo transfer patients.
Article in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The relationship between preimplantation hematological profiles and clinical pregnancy rates following frozen embryo transfer (FET) has not been thoroughly investigated. Methods: A retrospective study was conducted. Participants were categorized into clinical pregnancy (N = 404) and non-pregnancy (N = 313) groups. Novel hematological indices were evaluated using multivariable logistic regression, restricted cubic splines (RCS), and weighted quantile sum (WQS) regression to explore the relationship between exposure effects and clinical pregnancy outcomes. Least absolute shrinkage and selection operator (LASSO) regression was applied to select key predictors. Furthermore, we quantified absolute risk differences and assessed incremental predictive value [area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI)], utilizing sensitivity analyses to prevent statistical over-adjustment. Results: The non-pregnancy group showed higher levels of age, BMI, and inflammatory/insulin resistance markers. Spearman analysis indicated correlations between hormones (AMH, LH, E2, T) and specific novel hematological indices. Logistic regression indicated positive associations between Triglyceride glucose index (TyG Index), Triglyceride glucose-body mass index (TyG-BMI), and non-pregnancy. RCS analysis showed a nonlinear relationship for the TyG Index (P for overall<0.001, P for nonlinear = 0.031) and a linear relationship for TyG-BMI (P for overall<0.001, P for nonlinear = 0.174). Across TyG-BMI quartiles, the marginal predicted probability of clinical pregnancy was 63.1% in Q1 and 46.6% in Q4 (an absolute difference of 16.5%). The fully adjusted TyG-BMI model yielded an AUC of 0.677. Incorporating TyG-BMI into the baseline clinical model showed incremental predictive value in this dataset (NRI = 0.264, IDI = 0.020). In sensitivity analyses excluding baseline BMI, the association between TyG-BMI and non-pregnancy remained statistically significant. Finally, WQS regression assigned the highest weight to the TyG Index among insulin resistance indicators, and LASSO regression identified NPAR and TyG-BMI are associated with clinical pregnancy outcomes. Conclusions: The TyG Index and TyG-BMI are associated with clinical pregnancy outcomes of FET. Evaluating these preimplantation indices may help identify women at higher risk of non-pregnancy.
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