Knowledge Gaps in Placenta Accreta Spectrum

Predicting Placenta Accreta Spectrum Disorder Through Machine Learning Using Metabolomic and Lipidomic Profiling and Clinical Characteristics

OBJECTIVE

To perform metabolomic and lipidomic profiling with plasma samples from patients with placenta accreta spectrum (PAS) to identify possible biomarkers for PAS and to predict PAS with machine learning methods that incorporated clinical characteristics with metabolomic and lipidomic profiles.

METHODS

This was a multicenter case-control study of patients with placenta previa with PAS (case group n=33) and previa alone (control group n=21). Maternal third-trimester plasma samples were collected and stored at -80°C. Untargeted metabolomic and targeted lipidomic assays were measured with flow-injection mass spectrometry. Univariate analysis provided an association of each lipid or metabolite with the outcome. The Benjamini-Hochberg procedure was used to control for the false discovery rate. Elastic net machine learning models were trained on patient characteristics to predict risk, and an integrated elastic net model of lipidome or metabolome with nine clinical features was trained. Performance using the area under the receiver operating characteristic curve (AUC) was determined with Monte Carlo cross-validation. Statistical significance was defined at P<.05.

RESULTS

The mean gestational age at sample collection was 33 3/7 weeks (case group) and 35 5/7 weeks (control group) (P<.01). In total, 786 lipid species and 2,605 metabolite features were evaluated. Univariate analysis revealed 31 lipids and 214 metabolites associated with the outcome (P<.05). After false discovery rate adjustment, these associations no longer remained statistically significant. When the machine learning model was applied, prediction of PAS with only clinical characteristics (AUC 0.685, 95% CI, 0.65-0.72) performed similarly to prediction with the lipidome model (AUC 0.699, 95% CI, 0.60-0.80) and the metabolome model (AUC 0.71, 95% CI, 0.66-0.76). However, integration of metabolome and lipidome with clinical features did not improve the model.

CONCLUSION

Metabolomic and lipidomic profiling performed similarly to, and not better than, clinical risk factors using machine learning to predict PAS among patients with PAS with previa and previa alone.

Updates and Knowledge Gaps in Placenta Accreta Spectrum Biology

Placenta accreta spectrum (PAS) disorders have traditionally been characterized based on histopathologic grading, emphasizing the invasion of trophoblasts into the myometrium, and uterine serosa. Recent research has shifted the etiological understanding of PAS, moving away from the concept of aggressive trophoblast invasion to focusing on the critical role of scarred decidual-myometrial interface. This shift highlights the importance of defective scar tissue as a primary factor, reshaping prevention strategies, diagnostic accuracy, and treatment approaches for this increasingly prevalent iatrogenic and morbid pregnancy complication.

Biology and Pathophysiology of Placenta Accreta Spectrum Disorder

Placenta accreta spectrum (PAS) disorders present a significant clinical challenge, characterized by abnormal placental adherence to the uterine wall secondary to uterine scarring. With the rising global cesarean delivery rates, the incidence of this iatrogenic disorder has increased, underscoring the critical need for an understanding of its pathophysiology to inform management and prevention strategies. Normal placentation depends on tightly regulated extravillous trophoblast invasion into the decidua, spiral artery remodeling, interactions with the extracellular matrix, and immune modulation. Uterine scarring disrupts this balance, creating an environment deficient in key regulatory signals required for coordinated implantation and decidualization. In PAS, the loss of inhibitory decidual cues and deficient boundary limits permits unrestrained trophoblast into the abnormal decidual environment. Dysregulated signaling, along with an inflammatory milieu in scarred tissues, exacerbates abnormal placental development. Current prenatal imaging focuses on the appearance of excessive fibrinoid deposition, extracellular matrix remodeling, and incomplete spiral artery transformation as surrogates of PAS risk stratification. Emerging single-cell RNA sequencing and proteomic profiling offer insights into biomarkers and pathways that enable targeted interventions. Preventive efforts should prioritize reducing cesarean delivery rates to limit uterine scarring. Advances in regenerative medicine and bioengineering, including extracellular matrix-modulating biomaterials, growth factor therapies, and antifibrotic interventions, hold promise for improving scar healing and reducing PAS risk. This review bridges foundational science and clinical application, emphasizing the importance of the underlying placental biology and pathophysiology to make a clinical difference in detecting, treating, and preventing PAS. Addressing drivers of abnormal placentation is critical for improving maternal and neonatal outcomes with this increasingly prevalent iatrogenic condition.

Placenta accreta spectrum disorder at single-cell resolution: a loss of boundary limits in the decidua and endothelium

BACKGROUND

Placenta accreta spectrum disorders are associated with severe maternal morbidity and mortality. Placenta accreta spectrum disorders involve excessive adherence of the placenta preventing separation at birth. Traditionally, this condition has been attributed to excessive trophoblast invasion; however, an alternative view is a fundamental defect in decidual biology.

OBJECTIVE

This study aimed to gain insights into the understanding of placenta accreta spectrum disorder by using single-cell and spatially resolved transcriptomics to characterize cellular heterogeneity at the maternal-fetal interface in placenta accreta spectrum disorders.

STUDY DESIGN

To assess cellular heterogeneity and the function of cell types, single-cell RNA sequencing and spatially resolved transcriptomics were used. A total of 12 placentas were included, 6 placentas with placenta accreta spectrum disorder and 6 controls. For each placenta with placenta accreta spectrum disorder, multiple biopsies were taken at the following sites: placenta accreta spectrum adherent and nonadherent sites in the same placenta. Of note, 2 platforms were used to generate libraries: the 10× Chromium and NanoString GeoMX Digital Spatial Profiler for single-cell and spatially resolved transcriptomes, respectively. Differential gene expression analysis was performed using a suite of bioinformatic tools (Seurat and GeoMxTools R packages). Correction for multiple testing was performed using Clipper. In situ hybridization was performed with RNAscope, and immunohistochemistry was used to assess protein expression.

RESULTS

In creating a placenta accreta cell atlas, there were dramatic difference in the transcriptional profile by site of biopsy between placenta accreta spectrum and controls. Most of the differences were noted at the site of adherence; however, differences existed within the placenta between the adherent and nonadherent site of the same placenta in placenta accreta. Among all cell types, the endothelial-stromal populations exhibited the greatest difference in gene expression, driven by changes in collagen genes, namely collagen type III alpha 1 chain (COL3A1), growth factors, epidermal growth factor-like protein 6 (EGFL6), and hepatocyte growth factor (HGF), and angiogenesis-related genes, namely delta-like noncanonical Notch ligand 1 (DLK1) and platelet endothelial cell adhesion molecule-1 (PECAM1). Intraplacental tropism (adherent versus non-adherent sites in the same placenta) was driven by differences in endothelial-stromal cells with notable differences in bone morphogenic protein 5 (BMP5) and osteopontin (SPP1) in the adherent vs nonadherent site of placenta accreta spectrum.

CONCLUSION

Placenta accreta spectrum disorders were characterized at single-cell resolution to gain insight into the pathophysiology of the disease. An atlas of the placenta at single cell resolution in accreta allows for understanding in the biology of the intimate maternal and fetal interaction. The contributions of stromal and endothelial cells were demonstrated through alterations in the extracellular matrix, growth factors, and angiogenesis. Transcriptional and protein changes in the stroma of placenta accreta spectrum shift the etiologic explanation away from "invasive trophoblast" to "loss of boundary limits" in the decidua. Gene targets identified in this study may be used to refine diagnostic assays in early pregnancy, track disease progression over time, and inform therapeutic discoveries.