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

  • Dahiana M. Gallo EMAIL logo , Roberto Romero EMAIL logo , Mariachiara Bosco , Tinnakorn Chaiworapongsa , Nardhy Gomez-Lopez , Marcia Arenas-Hernandez , Eunjung Jung , Manaphat Suksai , Francesca Gotsch , Offer Erez and Adi L. Tarca
Published/Copyright: June 21, 2022
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Journal of Perinatal Medicine
From the journal Journal of Perinatal Medicine Volume 51 Issue 2

Abstract

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.

Highlights

  1. Pregnant women with severe PPH had higher maternal plasma concentrations of IL-16, IL-6, IL-12/IL-23p40, MCP-1, and IL-1β than patients without PPH.

  2. Maternal plasma cytokines could serve as biomarkers, or their pathways may be therapeutic targets in PPH.

Keywords: biomarker; CCL2; IL-1β; IL-12/IL-23p40; IL-16; IL-6; inflammation; interleukin (IL); maternal proteins; MCP-1

Introduction

Postpartum hemorrhage (PPH) is the leading cause of maternal morbidity and mortality worldwide, accounting for 27% of all maternal deaths [1], [2], [3], [4], [5], [6], [7], [8], [9], and the frequency of this condition has remained unchanged for the last 40 years in the United States [10]. Uterine atony is considered responsible for approximately 70–80% of cases of PPH [5, 11], [12], [13], followed by retained placenta [14], placenta accreta spectrum disorder [15, 16], perineal lacerations, and uterine inversion [5, 14], [15], [16], [17], [18]. Risk factors for PPH include clinical chorioamnionitis, prolonged labor, prolonged use of oxytocin, placental abruption, uterine overdistention, and coagulation disorders (e.g. Von Willebrand disease) [5, 11, 12, 19, 20].

The causes of uterine atony are poorly understood, and this condition is often attributed to an overdistended uterus or to one that becomes exhausted after prolonged labor or administration of oxytocin [11]. A role for acute inflammation as a cause of uterine atony and PPH has been proposed on the basis of pathological studies [20] reporting an excess of inflammatory cells (e.g. neutrophils, macrophages, and mastocytes), and an overexpression of C5a receptor-positive cells within the samples of myometrium obtained from patients with PPH [21, 22]. These findings, coupled with the observations that clinical chorioamnionitis is associated with PPH [23], [24], [25] and that intra-amniotic infection is present in over 60% of patients with clinical chorioamnionitis [26, 27], provide additional support for this hypothesis. The purpose of this study was to determine whether the maternal plasma cytokines concentration are different in women who subsequently develop PPH due to uterine atony.

Materials and methods

Study subjects

This retrospective case-control study included cases of PPH and a matched control group in a ratio of 2:1. Patients were matched for gestational age at delivery, labor status, delivery route, parity, and year of sample collection. Cases and controls were selected from the clinical database of Wayne State University, Detroit Medical Center, and the Perinatology Research Branch, which included women who delivered at Hutzel Women’s Hospital (Detroit, Michigan, USA), between 2001 and 2021. Maternal blood samples were collected within 3 days of delivery as part of an observational study of normal pregnant women and those with complications of pregnancy. All patients provided written informed consent prior to the collection of samples.

Ethical approval

The use of clinical database and biological samples was approved by the Institutional Review Boards of NICHD and Wayne State University.

Clinical definitions

Gestational age (GA) was determined by the last menstrual period and confirmed by ultrasound examination, or solely by the ultrasound crown-rump length measurement in the first trimester of pregnancy if the sonographic determination of gestational age was not consistent with menstrual dating [28].

Postpartum hemorrhage was defined as cumulative blood loss ≥1,000 mL or as blood loss accompanied by signs or symptoms of hypovolemia within 24 h after birth (including intrapartum blood loss) regardless of route of delivery and requiring blood transfusion [5, 29], [30], [31], [32]. All patients diagnosed with PPH in this study received a blood transfusion. We decided to use a stringent definition of PPH to identify the cases that were clinically important.

Sample collection and immunoassays

Plasma samples were collected within three days before delivery. Blood was placed in tubes containing EDTA. The samples were centrifuged at 4 °C, 1,300×g for 10 min, and stored at −70 °C. Laboratory personnel were blinded to the clinical diagnosis.

Determination of the concentrations of plasma cytokines

The V-PLEX Proinflammatory Panel 1 (human), Cytokine Panel 1 (human), and Chemokine Panel 1 (human) immunoassays (Meso Scale Discovery [MSD], Rockville, MD, USA) were used to measure the concentrations of cytokines/chemokines described in Table 1. Briefly, 50 μL of maternal plasma or calibrator were dispensed into the separate wells of the plates and incubated for 2 h with vigorous shaking at room temperature. The samples and calibrators were removed, and the plates were washed 3 times with the MSD 1× wash buffer, followed by the addition of 25 μL of the 1× detection antibody solution into each well. Plates were then incubated for 2 h with vigorous shaking at room temperature. The detection antibody was removed, and the plates were washed 3 times. Next, 150 μL of 2× Read Buffer T were added to each well, and the plates were read with the MESO QuickPlex SQ 120 (MSD), and analyte concentrations were calculated with the Discovery Workbench 4.0 (MSD). The assay characteristics are shown in Table 1.

Table 1:

The assay characteristics of each analyte.

Analytes Sensitivity, pg/mL Intra assay coefficient of variation, % Inter assay coefficient of variation, %
IFN-γ 0.3 3.3 4.4
IL-10 0.0 3.1 4.6
Il-12p70 0.1 3.4 9.9
IL-13 0.1 3.3 7.1
IL-1β 0.2 2.2 14.3
IL-2 0.2 3.0 2.2
IL-4 0.0 2.5 6.7
IL-6 0.1 2.8 5.9
IL-8 0.1 5.6 8.3
TNF-α 0.1 6.3 6.8
GM-CSF 0.2 2.9 5.8
IL-12/IL23p40 0.3 2.5 1.2
IL-15 0.1 5.1 10.2
IL-16 0.5 6.1 11.6
IL-17A 0.3 3.6 2.1
IL-1α 0.1 7.5 9.2
IL-5 0.1 2.0 9.5
IL-7 0.1 2.4 4.1
TNF-β 0.1 3.0 8.4
VEGF 0.1 2.9 7.6
Eotaxin 0.5 7.3 6.0
Eotaxin-3 0.9 4.3 7.4
CXCL10 0.1 3.2 4.5
MCP-1 0.1 4.5 8.8
MCP-4 0.2 4.0 8.3
MDC 1.0 2.9 5.4
MIP-1α 0.3 7.2 14.2
MIP-1β 0.2 4.9 4.7
TARC 0.4 8.2 7.9

  1. CXCL10, C-X-C motif chemokine ligand 10; GM-CSF, granulocyte macrophage colony-stimulating factor; IFN-γ, interferon gamma; IL, interleukin; MCP-1, monocyte chemotactic protein-1; MCP-4, monocyte chemotactic protein-4; MDC, macrophage-derived chemokine; MIP-1α, macrophage inflammatory protein-1α; MIP-1β, macrophage inflammatory protein-1β; TARC, thymus and activation-regulated chemokine; TNF-α, tumor necrosis factor-α; TNF-β, tumor necrosis factor-β; VEGF-A, vascular endothelial growth factor A.

Statistical analysis

Clinical characteristics were compared between cases and controls with a Fisher’s exact test, while continuous variables were compared by using the non-parametric Wilcoxon test. Protein concentration values below the detection limit for each protein were imputed with 99% of the lowest detected value, and logarithmic transformation was performed to improve the normality of the distribution. Cytokines’ data were compared between the groups by using linear mixed-effects models, allowing for a random effect to each matching block (one case and two controls). An adjusted analysis included maternal age, clinical chorioamnionitis, and preeclampsia as fixed-effects in these models. Linear mixed-effects models were fit by using the implement in the lme4 package [33] in R. Significance p-values for the group differences were adjusted by using the false discovery rate method to obtain q-values. A q-value <0.1 was considered a significant result, hence controlling the false discovery rate at the 10% level.

To evaluate the ability of proinflammatory mediators to predict PPH, we fit random forest models with data from all available proteins. A leave-one-out cross-validation validation (LOOCV) approach was used to train and test models, hence to avoid overstating the prediction performance. Alternatively, a 9-fold cross validation was used in which, by rotation, data from four matched blocks of patients (four cases and eight controls) are used for testing the model and the remainder of the dataset for training. The predicted risk scores were used to construct a Receiver Operating Characteristic (ROC) curve, and the AUC was determined with DeLong 95% confidence intervals. A secondary analysis evaluated the value of additional clinical and demographic characteristics in the proteomics prediction models, i.e. maternal age, body mass index, nulliparity, fetal sex, neonatal weight, clinical chorioamnionitis, and preeclampsia.

Results

Clinical characteristics of pregnant women with and without postpartum hemorrhage

The demographic characteristics of the patient population are shown in Table 2. By design, there were no differences in gestational age at delivery and venipuncture and sample storage time, among other characteristics shown in Table 2. Women with PPH were slightly older (median age 25 vs. 23 years) and more likely to have clinical chorioamnionitis (14 vs. 0%) and preeclampsia (14 vs. 0%) compared to controls.

Table 2:

Clinical and demographic characteristics of the study population.

Controls (n=72) Postpartum hemorrhage (n=36) p-Value
Maternal age, years 23 (20–27) 25 (22–32.2) 0.034
BMI, kg/m2 27.4 (22.8–29.9)a 29.5 (23.6–36.5)b 0.09
Smoking 7% (n=5) 13.9% (n=5) 0.3
Nulliparous 33.3% (n=24) 30.6% (n=11) 0.8
Spontaneous onset of labor 52.9% (n=36) 42.9% (n=15) 0.4
Cesarean delivery 29.2% (n=21) 27.8% (n=10) 1.0
Induction of labor 36.1% (n=26) 44.4% (n=16) 0.5
Gestational age at delivery, weeks 38.4 (37–40.1) 37.9 (36.5–40.1) 0.7
Birthweight, g 3,185 (2,672.5–3,517.5) 2,935 (2,456.2–3,372.5) 0.2
Birthweight percentile 47.9 (31.3–58.1) 33.5 (16.1–57.2) 0.06
Male sex 54.2% (n=39) 55.6% (n=20) 1.0
Clinical chorioamnionitis 0% (n=0) 13.9% (n=5) 0.0034
Acute histologic chorioamnionitis 15.2% (n=7) 21.9% (n=7) 0.6
Preeclampsia 0% (n=0) 13.9% (n=5) 0.0034
Gestational age at sample collection, weeks 38.3 (36.6–40) 37.8 (36.4–40) 0.7
Labor status at samplingc
 Not in labor 69.4% (n=50) 71% (n=22) 1.0
 Latent phase of labor 19.4% (n=14) 16.1% (n=5) 0.8
 Active phase of labor 11.1% (n=8) 12.9% (n=4) 0.7
Sample to delivery, h 9.5 (4.3–22.4) 15.1 (4.8–27.7) 0.3
Sample storage, years 12.1 (8.8–14) 11.8 (7.6–13.6) 0.9

  1. Data presented as median (interquartile range) and percentage and (n); BMI, body mass index; data for six patients missing. b: One datum missing; c: for five of the PPH cases, the labor status at sampling was not known.

Selected plasma cytokine concentrations are higher in women who subsequently develop postpartum hemorrhage

Maternal plasma concentrations of IL-16, IL-6, IL-12/IL-23p40, MCP-1, and IL-1β were higher in cases than in controls based on linear mixed-effects analysis in which the patient matching blocks were treated as random effects (q<0.1) (Figure 1, Table 3). The same five proteins were also identified as significant when treating the patient matching blocks as fixed effects in linear models (q<0.1). Next, we reanalyzed the data of the five significant proteins to determine whether differences in maternal age and frequency of clinical chorioamnionitis and preeclampsia between cases and controls could have affected the results. The adjusted log2 fold changes were similar to those obtained in the unadjusted analysis, and differences remained significant (q<0.1, Table 3). Similar conclusions were drawn after excluding cases with preeclampsia and clinical chorioamnionitis and corresponding matched controls instead of adjusting for these factors in the analysis. This approach suggests that the differences observed in the primary analysis could have not been explained by differences in these factors.

Figure 1: Maternal plasma protein concentrations in patients without postpartum hemorrhage (PPH) (controls) (n=72), and with PPH (n=36). Data are shown in boxplots. Logarithmic scales were used to improve normality of the distribution of the variables. p-values shown were derived using linear mixed-effect models to account for the random matching blocks. IL-1β does not show a normal distribution; for it, a non-parametric Wilcoxon test yielded a p-value of p=0.018.
Figure 1:

Maternal plasma protein concentrations in patients without postpartum hemorrhage (PPH) (controls) (n=72), and with PPH (n=36).

Data are shown in boxplots. Logarithmic scales were used to improve normality of the distribution of the variables. p-values shown were derived using linear mixed-effect models to account for the random matching blocks. IL-1β does not show a normal distribution; for it, a non-parametric Wilcoxon test yielded a p-value of p=0.018.

Table 3:

Comparison of log-transformed protein concentrations between postpartum hemorrhage cases and controls.

Proteins Crude analysis
Fold change Log2FC p-Value q-Value
IL-16 1.41 0.49 0.00001 0.003
IL-6 1.89 0.92 0.0003 0.004
IL-12/IL23p40 1.37 0.45 0.002 0.02
MCP-1 1.30 0.38 0.003 0.02
IL-1β 1.61 0.69 0.006 0.037
CXCL10 1.30 0.38 0.046 0.224
TNF-α 1.17 0.23 0.057 0.235
Eotaxin 1.14 0.19 0.083 0.263
IL-17A 1.24 0.31 0.085 0.263
VEGF 0.82 −0.29 0.091 0.263
IL-7 0.81 −0.31 0.15 0.40
MDC 1.09 0.13 0.17 0.42
IL-15 1.07 0.1 0.20 0.44
IL-2 0.72 −0.48 0.28 0.56
MIP-1α 1.27 0.34 0.29 0.56
IL-12p70 0.78 −0.35 0.32 0.58
IL-13 0.85 −0.23 0.39 0.67
IL-8 0.91 −0.14 0.48 0.69
Eotaxin-3 1.11 0.15 0.49 0.69
IL-1α 0.92 −0.12 0.50 0.69
IFN-γ 1.20 0.26 0.50 0.69
IL-4 1.07 0.09 0.61 0.80
MIP-1β 1.04 0.06 0.70 0.84
TARC 1.05 0.07 0.72 0.84
IL-5 1.06 0.09 0.75 0.84
GM-CSF 1.06 0.08 0.75 0.84
TNF-β 1.02 0.02 0.87 0.91
IL-10 1.03 0.04 0.88 0.91
MCP-4 1.00 0 0.98 0.98
Adjusted analysis
Fold change Log2FC p-Value q-Valuea
IL-16 1.37 0.46 0.001 0.007
IL-6 1.63 0.7 0.013 0.018
IL-12/IL23p40 1.32 0.4 0.014 0.018
MCP-1 1.20 0.27 0.056 0.055
IL-1β 1.67 0.74 0.01 0.018

  1. Differential concentrations results are listed in order of significance p-values. Variables included in the adjusted analyses were maternal age, clinical chorioamnionitis and preeclampsia. FC, Fold change. Positive log2FC correspond to higher concentration in PPH than controls. aFalse discovery rate adjustment for the five proteins of interest which were discovered based on the crude analysis. CXCL10, C-X-C motif chemokine ligand 10; GM-CSF, granulocyte macrophage colony-stimulating factor; IFN-γ, interferon gamma; IL, interleukin; MCP-1, monocyte chemotactic protein-1; MCP-4, monocyte chemotactic protein-4; MDC, macrophage-derived chemokine; MIP-1α, macrophage inflammatory protein-1α; MIP-1β, macrophage inflammatory protein-1β; TARC, thymus and activation-regulated chemokine; TNF-α, tumor necrosis factor-α; TNF-β, tumor necrosis factor-β; VEGF-A, vascular endothelial growth factor A.

A comparison between the current study and that of Jiang et al. [34] of differential protein abundance with PPH for the set of 19 proteins profiled in both studies revealed that IL-16 and IL-1β were increased with PPH in both studies, while other proteins were increased with PPH in only one of the two studies (IL6 and MCP-1 in the present study only; CXCL10, macrophage inflammatory protein-1 alpha (MIP-1α), tumor necrosis factor alpha (TNFα), and IL-1alpha (IL-1α) in Jiang et al. only) (Figure 2).

Figure 2: Comparison of differential cytokine concentrations in  postpartum hemorrhage (PPH) between this study and Jiang et al. The y-axis represents the fold changes (log, base 2, thereof) in concentration between PPH and controls. Positive values represent increased concentration in cases. The x-axis represents the late third trimester logistic model coefficients for the association between PPH and one log unit increase in concentration (Extracted from Table 5 in Jiang et al.). Positive x-axis values correspond to an increase in log-odds of PPH with increasing concentration of the protein.
Figure 2:

Comparison of differential cytokine concentrations in  postpartum hemorrhage (PPH) between this study and Jiang et al.

The y-axis represents the fold changes (log, base 2, thereof) in concentration between PPH and controls. Positive values represent increased concentration in cases. The x-axis represents the late third trimester logistic model coefficients for the association between PPH and one log unit increase in concentration (Extracted from Table 5 in Jiang et al.). Positive x-axis values correspond to an increase in log-odds of PPH with increasing concentration of the protein.

To assess the potential value of cytokine measurements to predict PPH, we fitted random forest models and evaluated them via leave-one-out cross-validation. The AUC (95% confidence interval) was 0.69 (0.58–0.81) (Figure 3A). Inclusion of maternal age, parity, fetal sex, neonatal weight, clinical chorioamnionitis, and preeclampsia only slightly increased the prediction performance [AUC, 0.71 (0.59–0.82)] (DeLong test, p=0.44) (Figure 3A). When a 9-fold cross-validation procedure was used instead, the AUC (95% confidence interval) was 0.73 (0.65–0.81) for proteins alone and 0.76 (0.64–0.87)] (DeLong test, p=0.74) (Figure 3B). The most salient predictor proteins in the random forest model fit with data from all samples were IL-16, IL-12/IL-23p40, MCP-1, and Eotaxin (Figure 4). The importance of the proteins in this analysis was determined based on their contribution to the discrimination between cases and controls in the multivariate random model (mean decrease in Gini Index, Figure 4). Of note, the first four of the top-ranked five proteins based on this multivariate analysis were the same as those identified in the univariate analyses (Figure 2).

Figure 3: Receiver Operating Characteristic (ROC) curve for prediction of postpartum hemorrhage. Leave-one-out (A) and 9-fold cross validated (B) predictions were obtained using the full set of proteins only (black line) or proteins and additional demographic and clinical variables (see Materials and methods) (red line). Area under the ROC curve (AUC) is given with 95% confidence intervals.
Figure 3:

Receiver Operating Characteristic (ROC) curve for prediction of postpartum hemorrhage. Leave-one-out (A) and 9-fold cross validated (B) predictions were obtained using the full set of proteins only (black line) or proteins and additional demographic and clinical variables (see Materials and methods) (red line). Area under the ROC curve (AUC) is given with 95% confidence intervals.

Figure 4: Variable importance plot for prediction of postpartum hemorrhage. The mean decrease in Gini Index represents a measure of the relative importance of the variables in the multi-variate random forest model used to predict postpartum hemorrhage.
Figure 4:

Variable importance plot for prediction of postpartum hemorrhage. The mean decrease in Gini Index represents a measure of the relative importance of the variables in the multi-variate random forest model used to predict postpartum hemorrhage.

Discussion

Pregnant women who subsequently developed PPH requiring blood transfusion had higher maternal plasma concentrations of IL-16, IL-6, IL-12/IL-23p40, MCP-1, and IL-1β than the controls. This difference remained significant after adjustment for the presence of clinical chorioamnionitis, preeclampsia, and maternal age. Our findings suggest a role for maternal inflammation in the pathogenesis of uterine atony and PPH and identify potential biomarkers and therapeutic targets.

Hemostasis after placental separation is achieved primarily through increased uterine contractility [35], [36], [37], [38], which generates the “living ligatures” [39, 40] and the formation of clots in the uterine circulation [41, 42]. Recognition of the importance of adequate myometrial contractility to achieve hemostasis is the basis for the practice of active management of the third stage of labor [43] and the treatment of PPH with uterotonic agents such as oxytocin [17, 32, 44], [45], [46], [47], [48], [49], [50], [51], [52], carbetocin [48, 51, 53, 54], ergot alkaloids [48, 50, 51, 55], and prostaglandins [44, 48], [49], [50], [51, 56, 57].

Many of the antenatal and intrapartum risk factors for PPH [12, 58] are thought to act through uterine atony (e.g. prolonged labor, oxytocin administration, etc.) [59, 60]. However, the precise etiologic mechanisms responsible for inadequate postpartum contractility have remained elusive. A role for infection and/or inflammation in uterine atony has been proposed for decades [23, 24], given the well-known association between clinical chorioamnionitis, uterine atony, and PPH [25]. Pathologic examination of peripartum hysterectomy specimens from women with uterine atony showed that acute inflammation is the most common lesion in both the uterus and the placenta delivered from such patients [20]. Myometrial acute inflammation has been identified in patients with massive PPH who required hysterectomy [21, 22]. Histologic examination has shown massive infiltration of the inflammatory cells such as neutrophils, mast cells, and macrophages [21]. Acute myometritis was detected in the fundus and in the isthmus, which appear to have a different role in defective hemostasis leading to PPH [21, 22]. In a series of systematic studies, Kanayama et al. found not only the presence of inflammatory cells but also edema, activation of complement (i.e. C5a receptor), and overexpression of the bradykinin receptor type 1 in the myometrium by using immunohistochemistry [61]. The functional effects of engagement of the C5a receptor and the bradykinin receptor type 1 in myometrial contractility have not been well characterized. They may operate through enhancing myometrial inflammation [62], [63], [64], [65], [66], [67], induction of edema [68, 69], or changes in the extracellular matrix, which act as a scaffold for myometrial cells during the contractile process [70].

Maternal concentrations of proinflammatory cytokines in patients who develop postpartum hemorrhage

Hernandez et al. [20] proposed that uterine atony may be the consequence of intraamniotic infection, which leads to the release of inflammatory cytokines able to impair uterine contractility. The authors called for the identification of specific cytokines to determine if this would allow for the prediction of PPH [20]. Jiang et al. [34] tested this hypothesis by investigating changes of maternal plasma cytokines in women who developed atonic PPH. The authors reported higher plasma concentrations of fibroblast growth factor (FGF), IL-1α, IL-1β, interleukin-1 receptor antagonist (IL-1ra), interleukin-2 receptor alpha (IL-2Rα), IL-16, IL-18, macrophage colony-stimulating factor (M-CSF), MIP-1α, beta-nerve growth factor (β-NGF), TNF-related apoptosis-inducing ligand (TRAIL), and stem cell factor (SCF) in the late third trimester in patients who developed PPH compared to those who did not present with PPH. A prediction model was constructed by using the maternal serum concentrations of FGF, MIP-1α, and SCF. This model had moderate performance in the prediction of atonic PPH (AUC, 0.84; sensitivity, 0.78; specificity, 0.83; positive likelihood ratio, 4.66; and negative likelihood ratio, 0.27) [34].

The results of our study support the findings of Jiang et al. and confirm an association between the concentrations of elevated serum/plasma cytokines and PPH. There are some differences between the two studies. Jiang et al. defined PPH as a blood loss greater than 500 mL within 24 h of vaginal delivery but did not require transfusion as a criterion in the selection of cases [34]. We chose either a measurement of 1,000 mL or the requirement of a blood transfusion and excluded other potential causes of PPH, such as placenta accreta spectrum disorder, placenta previa, uterine inversion, uterine rupture, and cervical lacerations, to focus our findings in patients who were likely to have uterine atony. Two of the top three most dysregulated proteins by magnitude of change (IL-16 and IL-1β) were found to be elevated in our study and in the study by Jiang et al. Other differences in the analytes identified in both studies can be explained, in part, by the differences in the protein panels used in each study, characteristics of the patient populations, underlying causes of PPH, and the time of sample collection (e.g. Jiang et al. reported a longitudinal study in the first and in the early and late third trimester, while our study was cross-sectional and focused on samples collected within 3 days of delivery).

Potential mechanisms linking inflammation and uterine atony

The mechanisms whereby maternal inflammation predisposes to uterine atony are unknown. Uterine inflammation due to intraamniotic infection is associated with altered myometrial function and PPH [23], [24], [25, 71], [72], [73], [74], [75], [76], [77]. Indeed, Zackler et al. reported that uterine contractility decreased 2 h after the onset of maternal fever during labor in patients with suspected clinical chorioamnionitis (assessed with an intrauterine pressure catheter) and that 32% of them developed PPH [25]. Moreover, the authors also reported a significant decrease in the myometrial response to oxytocin in patients with intrapartum fever. Oxytocin increased contractility 0.6 ± 0.1 Montevideo Unit (MVU)/mU before the diagnosis of chorioamnionitis but only 0.4 ± 0.1 MVU/mU after the diagnosis (p<0.001) [25].

Proinflammatory cytokines, such as IL-1 and TNF-α, stimulate uterine contractility and have been implicated in the mechanisms responsible for the onset of preterm parturition in the context of intra-amniotic infection [78], [79], [80], [81], [82] and spontaneous labor at term [83, 84]. These observations would appear to conflict with the clinical findings that maternal fever and clinical chorioamnionitis are associated with decreased uterine contractility at term and an increased risk of PPH. How can these findings be reconciled? Cierny et al. reported that the relationship between the concentrations of maternal serum cytokines and the progress of labor is complex and likely to be bimodal [84]. Cervical dilatation progresses more slowly during the latent phase of labor in patients with an IL-6 concentration in the first quartile, but a slower progression in cervical dilatation in the active phase was observed in patients with concentrations of TNF-α and IL-1 in the fourth quartile [84]. Therefore, proinflammatory cytokines may play a permissive role in the early phase of parturition [83], [84], [85], [86], [87], [88], [89], but exaggerated inflammation may impair myometrial contractility and labor progress and predispose to PPH [84, 90]. This proposal is consistent with the observation derived from the transcriptomic analysis of the myometrium in early spontaneous labor [91], [92], [93], [94], [95] and from patients who failed to progress in labor [90, 96]. The transition from uterine quiescence to spontaneous regular uterine contractility in early spontaneous labor is associated with an inflammatory signature in the myometrium [97], [98], [99], [100] and a more intensive inflammatory response in cases of an arrest of dilatation [96] and an arrest of descent [90]. The precise mechanism whereby inflammation may stimulate uterine contractility in some circumstances and impair myometrial function in others remains to be determined.

The role of cytokines in the regulation of smooth muscle contractility has been the objective of several investigations, although gaps in knowledge still exist, in particular, about the myometrium. We will briefly review what is known about the effect of cytokines in smooth muscle contractility and, in particular, of those cytokines found to be significantly elevated in this study (i.e. IL-1β, IL12/IL-23p40, IL-16, IL-6, and MCP-1 or CCL2). IL-1β is a proinflammatory cytokine that has induced uterine contractility in an in vitro model of extracorporeal circulation [101], in pregnant mice [102], and in primates [103]. This effect is blocked by the administration of the natural IL-1 receptor antagonist [104]. IL-1 induces myometrial contractility by stimulating the production of prostaglandins [105], [106], [107], [108], [109]. However, this cytokine also decreases uterine contractility through the activity of nitric oxide synthase (NOS) [110]. IL-1β has also been shown to downregulate the expression of oxytocin receptors on cultured human uterine myocytes in a time-dependent fashion [111]. Chronic exposure to IL-1β appears to decrease the activity of the myometrial oxytocin signaling [111, 112].

IL-6 is a major mediator of the acute phase response to tissue injury and has been implicated in term [113, 114] and preterm parturition [81, 115, 116]. However, in vitro studies have also shown that IL-6 can decrease uterine contractility over time, and this finding may be relevant for PPH [117]. IL-12/IL-23p40 is a heterodimer cytokine that interacts with the IL-23R/IL-12Rβ1 to contribute to Th17 differentiation and IL-17 production [118, 119]. IL-17 is the signature effector of Th17 cells and has been implicated in altering smooth muscle cell contractility in the airways and in the gut [120], [121], [122], [123], [124]. However, a paucity of data exists regarding the effect of IL-12/IL-23p40 on the myometrium. IL-16 is a proinflammatory cytokine that promotes the recruitment of lymphocytes and eosinophils to sites of inflammation [125], [126], [127], [128], [129]. IL-16 has been implicated in preterm parturition with intraamniotic infection [129] and in term patients with clinical chorioamnionitis and with evidence of intraamniotic inflammation [130]. However, there is no adequate information on the effect of IL-16 on myometrial contractility. MCP-1, or CCL2, is a chemokine known to recruit monocytes and eosinophils [131], [132], [133], [134]. The role of MCP-1 in term labor and preterm labor has been extensively described [135], [136], [137], [138]. MCP-1 is a key molecule implicated in the generation of myometrial inflammation during parturition and in myometrial contractility [139]. The administration of broad-spectrum chemokine inhibitors, which block the secretion of IL-8 and MCP-1, inhibited myocyte contractions in an in vitro study [140]. However, the possible pathophysiologic mechanism linking MCP-1/CCL2 to atonic PPH remains to be elucidated.

Clinical implications

The majority of patients with PPH do not have identifiable risk factors [12, 24, 59, 60], and no effective method for the prediction of this condition has been developed [141, 142]. The non-invasive assessment of inflammatory markers in the maternal plasma may be clinically useful for an early identification of women at risk for PPH and for therapeutic purposes. Anti-inflammatory agents are widely used in medicine, and anti-cytokine therapeutic [143], [144], [145], [146] interventions could represent a new approach to prevent PPH in a select group of patients or for the treatment of patients who are refractory to conventional management. Indeed, anti-cytokine agents (e.g. tocilizumab) have been used as adjunctive therapy to treat exaggerated inflammation in the context of SARS-CoV-2 infection in pregnant women [147, 148].

Research implications

This study demonstrates a possible relationship between an increased concentration of maternal plasma cytokines in pregnant women and the subsequent development of PPH. These cytokines could serve as potential biomarkers for the identification of women at risk of PPH who require transfusion, but further studies applying high-dimensional biology techniques such as proteomics, lipidomics, metabolomics, and transcriptomics, are needed to expand the pool of candidate biomarkers so that predictive accuracy can be improved.

Strengths and limitations

This study has several strengths. First, the definition of PPH required the administration of a blood transfusion, thus selecting the most severe cases. Second, samples of maternal blood were collected within 3 days of delivery. This criterion was meant to preserve a meaningful temporal relationship between the assessment of cytokines in maternal blood and PPH. Third, vaginal delivery and cesarean delivery were included and matched with the appropriate controls. Sub-analyses were performed to explore the effects of clinical chorioamnionitis and preeclampsia. Fourth, members of the laboratory team were blinded to the patient diagnosis, decreasing the possibility of biases in the analysis. Other strengths of the study included the fact that the analyses involved control of the false positive rate at the 10% level similar to other studies in the field [149], [150], [151]. This means that of the five proteins we claimed significant (q<0.1), it is expected that about 10%, i.e. (not even one) is a false-positive finding.

Limitations of the study included the moderate sample size as well as the presence of obstetrical complications (e.g. preterm labor, clinical chorioamnionitis, and preeclampsia) that are associated with increased concentrations of maternal plasma cytokines. However, adjustment suggested that these conditions could not have been explained by the findings. Another limitation is that the assays for seven of the 29 proteins studied had a coefficient of variation above 20%; therefore, it is possible that the value of cytokines may improve with more targeted and precise assays. For example, even though IL-1β was significantly higher in patients with PPH, the coefficient of variation for this cytokine was 39.9%. Therefore, the findings reported in this study need to be examined by using targeted assays, either chemically or immunologically based.

Conclusions

Pregnant women who subsequently developed severe PPH have higher maternal plasma concentrations of several proinflammatory cytokines, namely IL-16, IL-6, IL-12/IL-23p40, MCP-1, and IL-1β, compared to patients without PPH. Our findings support the concept that preexisting inflammation is a risk factor for uterine atony. Further studies are required to identify additional biomarkers and to improve the predictive performance of those already identified. The findings reported herein also identify potential therapeutic targets.

Corresponding authors: Roberto Romero, MD, DMedSci, Perinatology Research Branch, NICHD, NIH, DHHS, Hutzel Women’s Hospital, 3990 John R, 4 Brush, Detroit, MI 48201, USA; Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, MI, USA; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA; Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, MI, USA; and Detroit Medical Center, Detroit, MI, USA, Phone: (313) 993-2700, Fax: (313) 993-2694, E-mail: ; and Dahiana M. Gallo, MD, PhD, Perinatology Research Branch, NICHD, NIH, DHHS, Hutzel Women’s Hospital, 3990 John R, 4 Brush, Detroit, MI 48201, USA; and Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI, USA, Phone: (313) 993-2700, Fax: (313) 993-2694, E-mail:

Funding source: Eunice Kennedy Shriver National Institute of Child Health and Human Development

Award Identifier / Grant number: Contract No. HHSN275201300006C

  1. Research funding: This research was supported, in part, by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS); and, in part, with Federal funds from NICHD/NIH/DHHS under Contract No. HHSN275201300006C. The study was also supported, in part, by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health (Nardhy Gomez-Lopez, PhD, and Adi L. Tarca, PhD). Dr. Romero has contributed to this work as part of his official duties as an employee of the United States Federal Government.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflicts of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: Research involving human subjects complied with all relevant national regulations and institutional policies; is in accordance with the tenets of the Helsinki Declaration (as revised in 2013); and has been approved by the Institutional Review Boards of NICHD and Wayne State University.

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Received: 2022-05-02
Accepted: 2022-05-23
Published Online: 2022-06-21
Published in Print: 2023-02-23

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Is lowering of maternal mortality in the world still only a “dream within a dream”?
  4. Articles
  5. International Academy of Perinatal Medicine (IAPM) guidelines for screening, prediction, prevention and management of pre-eclampsia to reduce maternal mortality in developing countries
  6. Why maternal mortality in the world remains tragedy in low-income countries and shame for high-income ones: will sustainable development goals (SDG) help?
  7. Maternal mortality in the city of Berlin: consequences for perinatal healthcare
  8. New Jersey maternal mortality dashboard: an interactive social-determinants-of-health tool
  9. The study of healthcare facility utilization problems faced by pregnant women in urban north India
  10. Impediments to maternal mortality reduction in Africa: a systemic and socioeconomic overview
  11. Reducing maternal mortality: a 10-year experience at Mpilo Central Hospital, Bulawayo, Zimbabwe
  12. Strategies for the prevention of maternal death from venous thromboembolism clinical recommendations based on current literature
  13. Maternal plasma cytokines and the subsequent risk of uterine atony and postpartum hemorrhage
  14. What is already done by different societies in reduction of maternal mortality? Are they successful at all?
  15. Use and misuse of ultrasound in obstetrics with reference to developing countries
  16. Biological therapies in the prevention of maternal mortality
  17. Pre-eclampsia and maternal health through the prism of low-income countries
  18. Comparison of in-hospital mortality of COVID-19 between pregnant and non-pregnant women infected with SARS-CoV-2: a historical cohort study
  19. How does COVID-19 affect maternal and neonatal outcomes?
https://doi.org/10.1515/jpm-2022-0211
Keywords for this article
biomarker; CCL2; IL-1β; IL-12/IL-23p40; IL-16; IL-6; inflammation; interleukin (IL); maternal proteins; MCP-1

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Is lowering of maternal mortality in the world still only a “dream within a dream”?
  4. Articles
  5. International Academy of Perinatal Medicine (IAPM) guidelines for screening, prediction, prevention and management of pre-eclampsia to reduce maternal mortality in developing countries
  6. Why maternal mortality in the world remains tragedy in low-income countries and shame for high-income ones: will sustainable development goals (SDG) help?
  7. Maternal mortality in the city of Berlin: consequences for perinatal healthcare
  8. New Jersey maternal mortality dashboard: an interactive social-determinants-of-health tool
  9. The study of healthcare facility utilization problems faced by pregnant women in urban north India
  10. Impediments to maternal mortality reduction in Africa: a systemic and socioeconomic overview
  11. Reducing maternal mortality: a 10-year experience at Mpilo Central Hospital, Bulawayo, Zimbabwe
  12. Strategies for the prevention of maternal death from venous thromboembolism clinical recommendations based on current literature
  13. Maternal plasma cytokines and the subsequent risk of uterine atony and postpartum hemorrhage
  14. What is already done by different societies in reduction of maternal mortality? Are they successful at all?
  15. Use and misuse of ultrasound in obstetrics with reference to developing countries
  16. Biological therapies in the prevention of maternal mortality
  17. Pre-eclampsia and maternal health through the prism of low-income countries
  18. Comparison of in-hospital mortality of COVID-19 between pregnant and non-pregnant women infected with SARS-CoV-2: a historical cohort study
  19. How does COVID-19 affect maternal and neonatal outcomes?
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