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Multi-omics profiling identifies molecular associations in recurrent pregnancy lossMulti-omics profiling reveals new links to recurrent pregnancy loss

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Key Takeaway
Note that multi-omics associations in recurrent pregnancy loss require longitudinal validation and multicenter reproducibility.

This review explores the application of multi-omics profiling to characterize the mechanistic heterogeneity of recurrent pregnancy loss (RPL). The authors synthesize data across various layers of biological information, including genomics, epigenomics, single-cell and spatial transcriptomics, proteomics, metabolomics, microbiome analyses, and immunomics.

Key findings highlight several molecular associations. Genomic analyses identified chromosomal abnormalities and pathogenic variants associated with early embryonic developmental failure. Epigenomic profiling highlighted aberrant methylation patterns and imprinting disturbances. Furthermore, single-cell and spatial transcriptomics revealed altered cellular composition and disrupted communication among decidual stromal cells, uNK cells, macrophages, Treg, Th17 cells, and trophoblast lineages. Proteomic and metabolomic studies identified immune-metabolic signatures associated with impaired trophoblast function and vascular remodeling.

The authors note significant limitations in the current landscape, including small cohort sizes, particularly in single-cell datasets, and cross-platform heterogeneity. There is also a lack of multicenter reproducibility and insufficient longitudinal validation.

While these findings do not establish definitive causation, multi-omics integration may improve RPL subtype classification and risk prediction. Such advancements could potentially support immune-informed risk assessment and individualized management strategies.

This review examines how different biological layers, known as multi-omics, might help explain recurrent pregnancy loss. The review looks at existing data from various studies, including genomics, proteomics, and microbiome analyses, to find patterns in how pregnancy loss occurs.

The findings show several links. Researchers found associations between chromosomal abnormalities and early embryonic failure. They also identified unusual methylation patterns and changes in how certain immune cells, such as macrophages and T cells, communicate with each other. Additionally, the data suggests that immune and metabolic signatures may be linked to issues with how the placenta develops.

It is important to note that this is a review of existing literature, not a single new clinical trial. The current evidence is limited by small study sizes, a lack of long-term follow-up, and the need for more large-scale testing across different centers. Because these studies show associations rather than direct causes, the results are not yet ready to change standard medical practice.

While these findings are an important step toward identifying specific subtypes of pregnancy loss, more research is needed to confirm these patterns and turn them into reliable tools for patient care.

What this means for you:
Complex biological patterns are linked to recurrent pregnancy loss, but more large-scale studies are needed.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
Recurrent pregnancy loss (RPL) is a heterogeneous reproductive disorder in which dysregulation of maternal–fetal immune tolerance, aberrant decidual immune remodeling, and altered inflammasome signaling have been implicated within a complex multi-omics landscape. Multi-omics profiling (genomics, epigenomics, single-cell/spatial transcriptomics, proteomics, metabolomics, microbiome analyses, and immunomics) is increasingly being used to characterize mechanistic heterogeneity in RPL and to support biomarker discovery and immune-informed stratification. Genomic studies have associated chromosomal abnormalities and pathogenic variants with early embryonic developmental failure, while epigenomic profiling has highlighted aberrant methylation patterns and imprinting disturbances. Single-cell and spatial transcriptomics have revealed altered cellular composition and disrupted communication among decidual stromal cells, uterine natural killer (uNK) cells, macrophages, regulatory T cells (Treg), T helper 17 (Th17) cells, and trophoblast lineages. Proteomic and metabolomic studies have further identified immune–metabolic signatures associated with impaired trophoblast function and vascular remodeling, while emerging microbiome studies suggest a gut–reproductive axis that may modulate systemic immune homeostasis. Integration of multi-omics datasets with computational frameworks (e.g., weighted gene co-expression network analysis (WGCNA), multi-omics factor analysis (MOFA), and deep-learning models may improve RPL subtype classification, risk prediction, and the identification of potentially actionable pathways. However, current studies remain limited by small cohort sizes, especially in single-cell datasets, cross-platform heterogeneity, insufficient longitudinal validation, and a lack of multicenter reproducibility. Future work should prioritize standardized multi-omics pipelines, clearer evidence stratification, and immune-centric analytical frameworks to improve the robustness and translational relevance of RPL research. These advances may ultimately support immune-informed risk assessment and contribute to the gradual development of more individualized management strategies for RPL.
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