Is there a relationship between plasma, cytokine concentrations, and the subsequent risk of postpartum hemorrhage?

Oxidative stress-mediated abnormal polarization of decidual macrophages promotes the occurrence of atonic postpartum hemorrhage

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide. However, the mechanism underlying atonic PPH remains partially elucidated. Multi-omics revealed that differentially expressed proteins and metabolites were enriched in the immune-inflammation pathway in the vaginal blood of patients with atonic PPH. There was a pro-inflammatory immune microenvironment primarily activated by M1 macrophages in the decidua of the patients with atonic PPH, which presented as increased tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-8 levels and affected the contraction of the uterine smooth muscle. Besides, the decidual macrophage of the atonic PPH group exhibited increased oxidative stress. The PPH decidual cell culture medium induced the polarization of peripheral blood monocytes towards M1 macrophages while markedly increasing the levels of reactive oxygen species and superoxide anion radical. Using hydrogen peroxide (H2O2) to stimulate decidual macrophages induced a similar polarization state to that in atonic PPH samples, and the secretion of pro-inflammatory cytokines, such as TNF-α and IL-8, was significantly upregulated, which markedly impacted the expression of contraction-associated proteins (CAPs) in the uterine smooth muscle cells (uSMCs). The animal model suggested that H2O2 promoted the polarization of placental macrophages towards M1, affecting the levels of placental oxidative stress and inflammatory infiltration, and the contractility of uterine smooth muscle tissues. In summary, abnormal oxidative stress at the maternal-fetal interface induced the M1 polarization of decidual macrophages, causing the secretion of pro-inflammatory cytokines. TNF-α and IL-8 acted on uSMCs to inhibit CAP expression, inducing atonic PPH.

Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review

OBJECTIVES

To evaluate whether postpartum haemorrhage (PPH) can be predicted using both machine learning (ML) and traditional statistical models.

DESIGN

Diagnostic systematic review and meta-analysis of observational and clinical studies, prospectively registered on PROSPERO, performed accordingly to the Preferred Reporting Items for Systematic Reviews and Meta-analysis and Prediction model risk of bias assessment tool for studies developing, validating or updating prediction models, with the use of an independent analysis by a large language model (GPT-4 Open AI).

DATA SOURCES

MEDLINE/PubMed, LILACS-BVS, Cochrane Library, Scopus-Elsevier, Embase-Elsevier and Web of Science.

ELIGIBILITY CRITERIA FOR SELECTED STUDIES

The literature search was conducted on 4 January 2024 and included observational studies and clinical trials published in the past 10 years that assessed early PPH and PPH prediction and that applied accuracy metrics for outcomes evaluation. We excluded studies that did not define PPH or had exclusive PPH subgroups evaluation.

PRIMARY AND SECONDARY OUTCOME MEASURES

The primary outcome is the accuracy of PPH prediction using both ML and conventional statistical models. A secondary outcome is to describe the strongest risk factors of PPH identified by ML and traditional statistical models.

RESULTS

Of 551 citations screened, 35 studies were eligible for inclusion. The synthesis gathered 383 648 patients in 24 studies conducted with conventional statistics (CS), 9 studies using ML models and 2 studies using both methods. Multivariate regression was a preferred modelling approach to predict PPH in CS studies, while ML approaches used multiple models and a myriad of features. ML comparison to CS was only performed in two studies, and ML models demonstrated a 95% higher likelihood of PPH prediction compared with CS when applied to the same dataset (OR 1.95, 95% CI 1.88 to 2.01, p<0.001). The I² had a value of 54%, p=0.14, indicating moderate heterogeneity between the studies.

CONCLUSIONS

ML models are promising for predicting PPH. Nevertheless, they often require a large number of predictors, which may limit their applicability or necessitate automation through digital systems. This poses challenges in resource-scarce settings where the majority of PPH complications occur.

PROSPERO REGISTRATION NUMBER

CRD42024521059.

Novel predictive biomarkers for atonic postpartum hemorrhage as explored by proteomics and metabolomics

BACKGROUND

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality worldwide, with uterine atony accounting for approximately 70% of PPH cases. However, there is currently no effective prediction method to promote early management of PPH. In this study, we aimed to screen for potential predictive biomarkers for atonic PPH using combined omics approaches.

METHODS

Collection of cervicovaginal fluid (CVF) samples from 27 women with atonic PPH and 32 women with normal delivery was performed for metabolomic (LC-MS/MS) and proteomic (LC-MS/MS) detection and subsequent confirmation experiments in this nested case-control study. Mass spectrum and enzyme-linked immunosorbent assays (ELISA) were used to validate significantly different metabolites and proteins for screening potential biomarkers of atonic PPH. Furthermore, multivariate logistic regressions were performed for the prediction of PPH using the identified biomarkers mentioned above, and the area under the curve (AUC) was computed.

RESULTS

We identified 216 and 311 metabolites under positive and negative ion modes, respectively, as well as 1974 proteins. The PPH group had significant differences in metabolites and proteins belonging to the β-alanine metabolic pathway. Specifically, the PPH group had downregulation of critical metabolites, including histidine and protein dihydropyrimidine dehydrogenase (DPYD). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analysis of significantly differentially expressed proteins revealed that atonic PPH was associated with T cell- and macrophage-related immune inflammatory responses. Furthermore, we verified that concentrations of histidine (350.85 ± 207.87 vs. 648.33 ± 400.87) and DPYD (4.01 ± 2.56 vs. 10.96 ± 10.71), and immune cell-related proteins such as CD163 (0.29 ± 0.19 vs. 1.51 ± 0.83) and FGL2 (5.98 ± 4.23 vs. 11.37 ± 9.42) were significantly lower in the PPH group. Finally, the AUC for independent prediction of PPH using CD163, histidine, DPYD, and FGL2 are 0.969 (0.897-1), 0.722 (0.536-0.874), 0.719 (0.528-0.864), and 0.697 (0.492-0.844), respectively. A relatively high predictive efficiency was obtained when using joint histidine, DPYD, CD163, and FGL2, with AUC = 0. 964 (0.822-1).

CONCLUSIONS

This study suggested that immune inflammation may play a role in the occurrence of PPH. The metabolite histidine and proteins of DPYD, CD163, and FGL2 in CVF were associated with uterine atony and could be used as predictive biomarkers for atonic PPH.

Immune changes in pregnancy: associations with pre-existing conditions and obstetrical complications at the 20th gestational week-a prospective cohort study

BACKGROUND

Pregnancy is a complex biological process and serious complications can arise when the delicate balance between the maternal and semi-allogeneic fetal immune systems is disrupted or challenged. Gestational diabetes mellitus (GDM), pre-eclampsia, preterm birth, and low birth weight pose serious threats to maternal and fetal health. Identification of early biomarkers through an in-depth understanding of molecular mechanisms is critical for early intervention.

METHODS

We analyzed the associations between 47 proteins involved in inflammation, chemotaxis, angiogenesis, and immune system regulation, maternal and neonatal health outcomes, and the baseline characteristics and pre-existing conditions of the mother in a prospective cohort of 1049 pregnant women around the 20th gestational week. We used Bayesian linear regression models to examine the impact of risk factors on biomarker levels and Bayesian cause-specific parametric proportional hazards models to analyze the effect of biomarkers on maternal and neonatal outcomes. We evaluated the predictive value of baseline characteristics and 47 proteins using machine-learning models and identified the most predictive biomarkers using Shapley additive explanation scores.

RESULTS

Associations were identified between specific inflammatory markers and several conditions, including maternal age and pre-pregnancy body mass index, chronic diseases, complications from prior pregnancies, and COVID-19 exposure. Smoking during pregnancy affected GM-CSF and 9 other biomarkers. Distinct biomarker patterns were observed for different ethnicities. Within obstetric complications, IL-6 inversely correlated with pre-eclampsia risk, while birth weight to gestational age ratio was linked to markers including VEGF and PlGF. GDM was associated with IL-1RA, IL-17D, and eotaxin-3. Severe postpartum hemorrhage correlated with CRP, IL-13, and proteins of the IL-17 family. Predictive modeling yielded area under the receiver operating characteristic curve values of 0.708 and 0.672 for GDM and pre-eclampsia, respectively. Significant predictive biomarkers for GDM included IL-1RA and eotaxin-3, while pre-eclampsia prediction yielded the highest predictions when including MIP-1β, IL-1RA, and IL-12p70.

CONCLUSIONS

Our study provides novel insights into the interplay between preexisting conditions and immune dysregulation in pregnancy. These findings contribute to our understanding of the pathophysiology of obstetric complications and the identification of novel biomarkers for early intervention(s) to improve maternal and fetal health.

The CCL5/CCR5/SHP2 axis sustains Stat1 phosphorylation and activates NF-κB signaling promoting M1 macrophage polarization and exacerbating chronic prostatic inflammation

BACKGROUND AND OBJECTIVE

Chronic prostatitis (CP) is a condition markered by persistent prostate inflammation, yet the specific cytokines driving its progression remain largely undefined. This study aims to identify key cytokines involved in CP and investigate their role in driving inflammatory responses through mechanistic and therapeutic exploration.

METHODS

A 48-cytokine panel test was conducted to compare the plasma cytokine profiles between participants with CP-like symptoms (CP-LS) and healthy controls. Experimental autoimmune prostatitis (EAP) models were used for functional validation, with further mechanistic studies performed through in vivo and in vitro assays. Pharmacological inhibition was applied using maraviroc, and pathway inhibitors to assess therapeutic potential.

RESULTS

Our analysis identified CCL5 as one of the most prominently elevated cytokines in CP-LS patients. Further validation in the EAP model mice confirmed elevated CCL5 levels, highlighting its role in driving prostatic inflammation. Mechanistic studies revealed that CCL5 interacts with the CCR5 receptor, promoting M1 macrophage polarization and activating key inflammatory signaling pathways, including Stat1 and NF-κB, as indicated by increased phosphorylation of Stat1 and p65. In vitro, CCL5 combined with LPS stimulation amplified these effects, further promoting M1 polarization. CCL5 also sustained Stat1 activation by inhibiting its dephosphorylation through reduced interaction with SHP2, leading to prolonged inflammatory signaling. Single-cell transcriptomics confirmed high CCR5 expression in macrophages, correlating with inflammatory pathways. Pharmacological inhibition of CCR5, or its downstream signaling, significantly reduced macrophage-driven inflammation both in vivo and in vitro.

CONCLUSION

These findings establish the CCL5/CCR5 axis as a critical driver of persistant prostatic inflammation and present it as a potential therapeutic target for CP.

Postpartum hemorrhage and postpartum depression: A systematic review and meta-analysis of observational studies

OBJECTIVE

To assess the postpartum depression (PPD) risk in women with postpartum hemorrhage (PPH) and moderators.

METHODS

We identified observational studies of PPD rates in women with versus without PPH in Embase/Medline/PsychInfo/Cinhail in 09/2022. Study quality was evaluated using the Newcastle-Ottawa-Scale. Our primary outcome was the odds ratio (OR, 95% confidence intervals [95%CI]) of PPD in women with versus without PPH. Meta-regression analyses included the effects of age, body mass index, marital status, education, history of depression/anxiety, preeclampsia, antenatal anemia and C-section; subgroup analyses were based on PPH and PPD assessment methods, samples with versus without history of depression/anxiety, from low-/middle- versus high-income countries. We performed sensitivity analyses after excluding poor-quality studies, cross-sectional studies and sequentially each study.

RESULTS

One, five and three studies were rated as good-, fair- and poor-quality respectively. In nine studies (k = 10 cohorts, n = 934,432), women with PPH were at increased PPD risk compared to women without PPH (OR = 1.28, 95% CI = 1.13 to 1.44, p < 0.001), with substantial heterogeneity (I2 = 98.9%). Higher PPH-related PPD ORs were estimated in samples with versus without history of depression/anxiety or antidepressant exposure (OR = 1.37, 95%CI = 1.18 to 1.60, k = 6, n = 55,212, versus 1.06, 95%CI = 1.04 to 1.09, k = 3, n = 879,220, p < 0.001) and in cohorts from low-/middle- versus high-income countries (OR = 1.49, 95%CI = 1.37 to 1.61, k = 4, n = 9197, versus 1.13, 95%CI = 1.04 to 1.23, k = 6, n = 925,235, p < 0.001). After excluding low-quality studies the PPD OR dropped (1.14, 95%CI = 1.02 to 1.29, k = 6, n = 929,671, p = 0.02).

CONCLUSIONS

Women with PPH had increased PPD risk amplified by history of depression/anxiety, whereas more data from low-/middle-income countries are required.

Novel biomarkers for prediction of atonic postpartum hemorrhage among 'low-risk' women in labor

Background

Postpartum hemorrhage (PPH) is the primary cause of maternal mortality globally, with uterine atony being the predominant contributing factor. However, accurate prediction of PPH in the general population remains challenging due to a lack of reliable biomarkers.

Methods

Using retrospective cohort data, we quantified 48 cytokines in plasma samples from 40 women diagnosed with PPH caused by uterine atony. We also analyzed previously reported hemogram and coagulation parameters related to inflammatory response. The least absolute shrinkage and selection operator (LASSO) and logistic regression were applied to develop predictive models. Established models were further evaluated and temporally validated in a prospective cohort.

Results

Fourteen factors showed significant differences between the two groups, among which IL2Rα, IL9, MIP1β, TNFβ, CTACK, prenatal Hb, Lymph%, PLR, and LnSII were selected by LASSO to construct predictive model A. Further, by logistic regression, model B was constructed using prenatal Hb, PLR, IL2Rα, and IL9. The area under the curve (AUC) values of model A in the training set, internal validation set, and temporal validation set were 0.846 (0.757-0.934), 0.846 (0.749-0.930), and 0.875 (0.789-0.961), respectively. And the corresponding AUC values for model B were 0.805 (0.709-0.901), 0.805 (0.701-0.894), and 0.901 (0.824-0.979). Decision curve analysis results showed that both nomograms had a high net benefit for predicting atonic PPH.

Conclusion

We identified novel biomarkers and developed predictive models for atonic PPH in women undergoing "low-risk" vaginal delivery, providing immunological insights for further exploration of the mechanism underlying atonic PPH.

Histological Features of Uterine Myometrial Dysfunction: Possible Involvement of Localized Inflammation

OBJECTIVE

The latest perspective suggests that elevated levels of inflammation and cytokines are implicated in atonic postpartum hemorrhage. Lipopolysaccharide (LPS) has been widely used to induce inflammation in animal models. Therefore, this study aimed to induce uterine inflammation using LPS to investigate whether local inflammation triggers dysfunction and atrophy in the myometrium, as well as the potential underlying molecular mechanisms involved.

METHODS

In vivo, an animal model was established by intraperitoneal injection of 300 μg/ kg LPS in rats on gestational day 21. Hematoxylin-eosin (H&E) staining and Masson staining were employed to determine morphological changes in the rat uterine smooth muscle. Enzyme-linked immunosorbent assay (ELISA) was used to detect inflammatory cytokines. Immunohistochemistry, tissue fluorescence, and Western blotting were conducted to assess the expression levels of the uterine contraction-related proteins Toll-like receptor 4 (TLR4) and the nuclear factor kappa-B (NF-κB) signaling pathway. In vitro, human uterine smooth muscle cells (HUtSMCs) were exposed to 2 μg/mL LPS to further elucidate the involvement of the TLR4/NF-κB signaling pathway in LPS-mediated inflammation.

RESULTS

In this study, LPS induced uterine myometrial dysfunction in rats, leading to a disorganized arrangement, a significant increase in collagen fiber deposition, and widespread infiltration of inflammatory cells. In both in vivo animal models and in vitro HUtSMCs, LPS elevated IL-6, IL-1β, and TNF-α levels while concurrently suppressing the expression of connexin 43 (Cx43) and oxytocin receptor (OXTR). Mechanistically, the LPS-treated group exhibited TLR4 activation, and the phosphorylation levels of p65 and IκBα were notably increased.

CONCLUSION

LPS triggered the TLR4/NF-κB signaling pathway, inducing an inflammatory response in the myometrium and leading to uterine myometrial dysfunction and uterine atony.

Systemic and cerebrospinal fluid biomarkers for tuberculous meningitis identification and treatment monitoring

IMPORTANCE

Tuberculous meningitis is a life-threatening infection with high mortality and disability rates. Current diagnostic methods using cerebrospinal fluid (CSF) samples have limited sensitivity and lack predictive biomarkers for evaluating prognosis. This study's findings reveal excessive activation of the immune response during tuberculous meningitis (TBM) infection. Notably, a strong negative correlation was observed between CSF levels of monokine induced by interferon-γ (MIG) and the CSF/blood glucose ratio in TBM patients. MIG also exhibited the highest area under the curve with high sensitivity and specificity. This study suggests that MIG may serve as a novel biomarker for differentiating TBM infection in CSF or serum, potentially leading to improved diagnostic accuracy and better patient outcomes.

Maternal plasma cytokines and the subsequent risk of uterine atony and postpartum hemorrhage

OBJECTIVES

To determine whether the maternal plasma concentrations of cytokines are higher in pregnant women with postpartum hemorrhage (PPH) compared to pregnant women without PPH.

METHODS

A retrospective case-control study included 36 women with PPH and 72 matched controls. Cases and controls were matched for gestational age at delivery, labor status, delivery route, parity, and year of sample collection. Maternal plasma samples were collected up to 3 days prior to delivery. Comparison of the plasma concentrations of 29 cytokines was performed by using linear mixed-effects models and included adjustment for covariates and multiple testing. A false discovery rate adjusted p-value <0.1 was used to infer significance. Random forest models with evaluation by leave-one-out and 9-fold cross-validation were used to assess the combined value of the proteins in predicting PPH.

RESULTS

Concentrations of interleukin (IL)-16, IL-6, IL-12/IL-23p40, monocyte chemotactic protein 1 (MCP-1), and IL-1β were significantly higher in PPH than in the control group. This difference remained significant after adjustment for maternal age, clinical chorioamnionitis, and preeclampsia. Multi-protein random forest proteomics models had moderate cross-validated accuracy for prediction of PPH [area under the ROC curve, 0.69 (0.58-0.81) by leave-one-out cross validation and 0.73 (0.65-0.81) by 9-fold cross-validation], and the inclusion of clinical and demographic information did not increase the prediction performance.

CONCLUSIONS

Pregnant women with severe PPH had higher median maternal plasma concentrations of IL-16, IL-6, IL-12/IL-23p40, MCP-1, and IL-1β than patients without PPH. These cytokines could serve as biomarkers or their pathways may be therapeutic targets.