Circulating microparticle proteins predict pregnancies complicated by 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.

Rational Design of Nanomedicine for Placental Disorders: Birthing a New Era in Women's Reproductive Health

The placenta is a transient organ that forms during pregnancy and acts as a biological barrier, mediating exchange between maternal and fetal circulation. Placental disorders, such as preeclampsia, fetal growth restriction, placenta accreta spectrum, and gestational trophoblastic disease, originate in dysfunctional placental development during pregnancy and can lead to severe complications for both the mother and fetus. Unfortunately, treatment options for these disorders are severely lacking. Challenges in designing therapeutics for use during pregnancy involve selectively delivering payloads to the placenta while protecting the fetus from potential toxic side effects. Nanomedicine holds great promise in overcoming these barriers; the versatile and modular nature of nanocarriers, including prolonged circulation times, intracellular delivery, and organ-specific targeting, can control how therapeutics interact with the placenta. In this review, nanomedicine strategies are discussed to treat and diagnose placental disorders with an emphasis on understanding the unique pathophysiology behind each of these diseases. Finally, prior study of the pathophysiologic mechanisms underlying these placental disorders has revealed novel disease targets. These targets are highlighted here to motivate the rational design of precision nanocarriers to improve therapeutic options for placental disorders.

Shedding of Syncytiotrophoblast-Derived Extracellular Vesicles Is Increased in Placenta Previa and Accreta Spectrum

Placenta accreta spectrum (PAS) refers to excessive placental invasion into the maternal uterus and it is associated with high risk of obstetric haemorrhage and adverse maternal-neonatal outcomes. Currently, no specific circulating biomarkers of PAS have been identified. Given that in PAS disorders, the depth and the extension of placental invasion into the uterus are expected to be increased, in this study, we analysed plasma levels of syncytiotrophoblast-derived extracellular vesicles (STBEVs) in women with placenta previa (PP), at a high risk of PAS disorders, and pregnant women with normal placentation. Venous blood samples were collected from 35 women with ultrasonographic diagnosis of PP and 35 women with normal placentation, matched for gestational age. Plasma samples were ultracentrifuged at 120.000 g to collect extracellular vesicles (EVs). To identify and quantify plasma placenta-derived EVs (or STBEVs), EVs were analysed by flow cytometry using a monoclonal antibody against placental alkaline phosphatase (PLAP). Plasma levels of STBEVs were significantly higher in PP patients compared to controls. Plasma levels of STBEVs in women with PP and PAS showed a trend to a higher concentration compared to women with PP without PAS, although not reaching a statistical significance. Circulating STBEVs are potential candidates as biological markers to be integrated to ultrasonography in the antenatal screening programme for PAS. More studies are needed to confirm our observation in a larger cohort of patients and to analyse a possible association between high circulating levels of STBEVs and PAS.