Multivariate model predicts immune imbalance in recurrent pregnancy loss and recurrent implantation failure

Abstract

Abstract: Recurrent pregnancy loss (RPL) and recurrent implantation failure (RIF) are thought to arise from distinct yet partially overlapping causes, with a substantial number of cases associated with immune system alterations. We hypothesized that a peripheral blood signature integrating natural killer (NK) cell receptor status, monocyte activation, myeloid-derived suppressor cell (MDSC) abundance, and regulatory T cell (TReg) levels would more accurately distinguish each disorder from non-pregnant healthy controls than any single biomarker. We enrolled 194 women and performed deep immunophenotyping of NK cells, monocytes, MDSC, and TReg. Variable selection was performed with the Boruta algorithm, followed by multivariate logistic regression modelling. For RPL, the final model included five biomarkers, achieving an area under the curve of 0.95 and an accuracy of 90.7%. For RIF, the model retained four biomarkers, yielding an area under the curve of 0.85 and an accuracy of 79.5%. Logistic regression was deliberately chosen to prioritize clinical interpretability and facilitate future translation into a point-based diagnostic score.

Lay summary: Pregnancy is a complex process that depends on a healthy balance in the immune system. In some women, repeated miscarriages or the failure of embryos to implant during fertility treatments may be linked to subtle problems in immune regulation. In this study, we analysed blood samples from women with these problems and from healthy volunteers. We measured the presence and activity of different immune cells, such as NK cells, monocytes, and cells that help control immune reactions. Using a statistical approach, we identified small sets of markers that could reliably tell apart each group. One model was able to correctly classify nine out of ten women with recurrent miscarriage, and another did the same for women with failed embryo implantation. These findings could lead to simple blood tests that help doctors identify immune-related causes of pregnancy problems and guide more personalized treatments in the future.

Keywords: immune dysregulation; logistic regression model; recurrent implantation failure; recurrent pregnancy loss.

Figure 1

Relevant NK cell surface markers in HC, RPL, and RIF. The box plots show median and interquartile range. Group comparisons were performed using the Kruskal–Wallis test with Dunn’s post hoc test. *P < 0.05; **P < 0.01; NS, not significant.

Figure 2

Monocyte subpopulations and surface marker expression in HC, RPL, and RIF. The box plots represent median and interquartile range. The Kruskal–Wallis test with Dunn’s post hoc test was used. Exact P-values are shown for statistical trends. *P < 0.05; **P < 0.01; NS, not significant.

Figure 3

Circulating relevant cytokine levels in HC, RPL, and RIF. Data are shown as box plots (median and interquartile range). Kruskal–Wallis test with Dunn’s post hoc test. Exact P-values indicate trends. *P < 0.05; **P < 0.01; NS, not significant.

Figure 4

Frequency of FlowSOM-defined clusters in HC, RPL, and RIF. The box plots show median and interquartile range. Kruskal–Wallis test with Dunn’s post hoc test. Exact P-values indicate trends. *P < 0.05; **P < 0.01; NS, not significant.

Figure 5

Overview of the analytical workflow.