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Finding the priority and cluster of inflammatory biomarkers for infectious preterm birth: a systematic review

Abstract

Infectious preterm birth (PTB) is one of the most important causes of perinatal death. It is difficult to find reliable biomarkers accurate to gestational weeks for infectious PTB prediction clinically. Infectious PTB is found usually accompanied with immune imbalance. Thus, the systematic study to find the priority of inflammatory biomarkers and innovative inflammatory clusters for infectious PTB prediction is urgently needed.

This systematic study that focused on the inflammatory clusters and infectious PTB in the PubMed database was analyzed by using the criteria of the Population, Intervention, Comparison, Outcome, and Study design (PICOS) framework according to the recommendations of preferred reporting items for systematic reviews and meta-analysis (PRISMA).

The network meta-analyzed results showed that the prioritization of the inflammatory factors for infectious PTB prediction is soluble tumor necrosis factor receptor 2 (sTNFR2) > tumor necrosis factor α (TNFα) > interleukin-10 (IL-10) > interleukin-6 (IL-6) > C-reactive protein (CRP) > interleukin-1β (IL-1β). Furthermore, the results also indicated that global consideration of multiple inflammatory factors, such as CRP/IL-1β/IL-6 biomarker cluster in gestational 27–34 weeks, and the tumor necrosis factor/nerve growth factor (TNF/NGF) family during gestational 25–33 weeks, were potential biomarker clusters that specific for infectious PTB prediction.

This study systematically pointed out prioritization of the inflammatory factors for infectious PTB prediction. The results also provided evidence that maternal inflammatory clusters can predict infectious PTB occurrence at accurate gestational week. The global consideration of multiple inflammatory factors at accurate gestational age is highlighted.

Background

One of the main causes of perinatal death is preterm birth (PTB), which is defined as a delivery before 37 weeks of gestation [1, 2]. According to the World Health Organization (WHO) research from 2019, 14.8 million newborns were born with PTB at a rate of 10.6% [3]. Infection and infection caused inflammation are the main causes of PTB [4].

Maternal infection can release inflammatory factors into the maternal peripheral circulation [4, 5]. Microorganisms can induction parturition under infectious condition [6], microorganisms and their products can activate the toll-like receptor and induce the production of chemokines, cytokines, prostaglandins, and proteases [7,8,9]. During intra-amniotic infection, the microorganisms can attack the fetus and cause brain or lung problems by triggering a fetal systemic inflammatory response. Under this circumstance, the infants born immaturely before term [10]. In addition to intra-amniotic infection, extra-uterine infections, such as malaria, can also induce PTB by creating an inflammatory imbalance [4].

The protein biomarkers are most common for infectious PTB. For example, the inflammatory factors that mentioned in this study included sTNFR2, TNFα, IL-10, IL-6, CRP, and IL-1β, they could be induced by infection, and highly expressed in infectious PTB [11,12,13,14,15,16,17,18,19]. The detection method of these protein markers is enzyme-linked immunosorbent assay. The other biomarkers for infectious PTB are not common. For example, microRNA-142 transcript is one of the microRNAs that associated with occurrence of PTB [20]. The detection method of microRNA biomarkers is transcriptome profiling after sampling. In addition, gene set is also found participated in occurrence of PTB [20], and the detection method is genome profiling after sampling. Based on this, the detection of protein biomarkers is much cheaper and more convenient than other biomarkers.

Inflammatory factors in maternal peripheral circulation can show the pregnant status to a certain extent. Over the last several decades, the expression levels of several inflammatory factors have been found to be significantly different between infectious PTB and normal pregnancy. sTNFR2 and TNFα have been found participated in infection caused inflammation [11, 12] and highly expressed in infectious PTB [13, 21]. Interleukins, such as IL-10, IL-6, and IL-1β are essential cytokines promoting inflammation after infection [16] and dramatically upregulated in infectious PTB patients [14, 15]. CRP significantly increases after infection [18], and always be found upregulation in infectious PTB patients [17]. Whether these inflammatory factors can be potential biomarkers to predict the occurrence of infectious PTB needs extensive analysis. This study systematically summarized the published evidence by meta-analysis to find the reliable inflammatory biomarkers for infectious PTB prediction. We found there were 17 acceptable randomized control trials analyzing the potential biomarkers for infectious PTB. We summarized that there were 19 inflammatory biomarkers inside, which included interleukins, interferons, and cytokines, etc. Among these 19 inflammatory biomarkers, there were 2 inflammatory biomarkers significantly upregulating in PTB maternal blood at the time of delivery and 17 inflammatory biomarkers significantly upregulating in infectious PTB maternal blood before delivery. In addition, among these 17 inflammatory biomarkers, CRP, IL-6, and IL-1β were the most frequently studied factors. We further found that CRP, IL-6, and IL-1β might be reliable inflammatory biomarkers for infectious PTB prediction by meta-analysis. Furthermore, CRP/IL-1β/IL-6 cluster were potential biomarkers for infectious PTB patients in gestational 27–34 weeks. The TNF/NGF cytokine family has also been found to participate in the occurrence of infectious PTB during gestational 25–33 weeks. This systematic review provided a fundamental basis for timely prediction and intervention to prevent infectious PTB clinically.

Methods

Literature search strategy

Before we did the systematic literature search, we searched for the topic of this study in the PROSPERO database to ensure no similar systematic review was available, and we established the new record in PROSPERO (PROSPERO ID: CRD42022345185). Then we conducted a systematic literature search in the PubMed database from inception until July 2022 to identify the relevant studies on the inflammation biomarkers for PTB patients. Inflammation and PTB related key words were the research terms that were used in this study (Supplementary data 1). We did the Medical Subject Headings (MeSH) for further analysis. The study language was restricted to English. Two authors removed duplicates and evaluated the records independently. The different opinions were discussed with a third author to make a decision.

Inclusion and exclusion criteria

The inclusion of the references all met the criteria of the Population, Intervention, Comparison, Outcome, and Study design (PICOS) framework according to the recommendations of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [22]. The inclusion criteria were as follows: (1) population: pregnancies with infectious PTB disorder; (2) intervention and comparison: inflammatory factors in maternal blood between normal and infectious PTB pregnancies; (3) outcome: the blood concentration of inflammatory factors in normal and infectious PTB pregnancies before delivery; and (4) study design: randomized controlled trials. In addition, studies without adequate data (n value, mean value, P value, standard deviation value) were excluded.

Data extraction

The following information was extracted from the 17 included studies: the first author, publication year, country, sampling time, sample size, and delivery gestational weeks, inflammatory factor, functions. In addition, the P values, means, standard deviation of the means, and sample size in each group were also extracted. In some studies, the statistical data was shown in graphs and bar charts rather than table. The GetData Graph Digitizer software was used to extract the data from the graphs or bar charts. Two authors independently extracted the data. The different opinions were discussed with a third author to make a decision.

Risk-of-bias and quality assessment

Two authors independently assess the quality of the included studies. Random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other bias were assessed as “low,” “high,” or “unclear” risk. The risk of bias graph was drawn by the software Review Manager 5.4 (The Cochrane Collaboration, UK). The different opinions were discussed with a third author to make a decision.

Data synthesis and analysis

The concentrations of different inflammatory factors were analyzed between normal and PTB pregnancies. Studies were divided into a normal pregnancy group and a PTB group. The extracted n value, mean value, and standard deviation value of two groups were imported into Stata MP16 software (Stata Corp, College Station, TX, USA) respectively. The command “db metan” can start forest plot analysis. The command “db metafunnel” can start funnel plot analysis. The command “db metaninf” can start sensitivity analysis. P-value < 0.05 was considered to be a significant difference. For network meta-analysis, the command “network map” was used for drawing the network, and the command “sucra prob*, lab(sTNFR2 TNFα IL10 IL6 CRP IL1β)” was used for surface under the cumulative ranking curve (SUCRA) value analysis. The size of the nodes and the thickness of the edges are weighted according to the number of studies evaluating each inflammatory factors and direct comparison in network figure. The cumulative probabilities rank the priority of inflammatory factors for PTB prediction. The curve of each inflammatory factors is the cumulative ranking curve lining out the SUCRA value.

Results

Study characteristics

The process of literature search in this study is outlined in Fig. 1. We did our initial search in PubMed databases and identified 1171 records. These records were imported into EndNote for further screening. There were no duplicated records among these records, and the review paper (n = 218) and meta-analysis (n = 11) were excluded by keyword and note screening. The remaining 942 records underwent further title and abstract screening, and 908 records were removed because of inappropriate study subjects (n = 112) and research purposes (n = 796). The full text of the remaining 34 studies was carefully reviewed, and 17 studies were excluded because of inappropriate study design (n = 11) or lack of adequate data (n = 6). Finally, 17 studies that met the inclusion criteria were included in the quantitative synthesis.

Fig. 1
figure 1

The flow diagram of this study selection process in the meta-analysis

Table 1 summarizes the detailed information for the remaining 17 articles. The country, sample origin, gestational weeks for sampling, delivery gestational weeks, significant differences, inflammatory factors, and function of the inflammatory factors, etc. were included.

Table 1 The detailed information for the included articles.

Study quality

The qualities of these 17 studies were analyzed in Fig. 2. For the selection bias, 12 studies described the specific methods of randomization. Seven studies on performance bias indicated that double-blind (participants and personnel) was used in these studies. Although the detailed methods of blinding for outcome assessment were not mentioned, the objective measurement of the inflammatory factors is related to double-blinding of participants and personnel. Therefore, the detection of bias was related to performance bias. For attrition bias, 17 studies have complete outcome data. For reporting bias, all the studies have no selective experimental data for high risk. For other bias, 9 studies were judged to have a high risk, including those with a small sample size. Overall, about 71% studies have low risk of selection bias, and 29% studies have unclear risk of selection bias. About 41% studies have low risk of performance bias, and 51% studies have unclear risk of performance bias. About 29% studies have low risk of detection bias, and 71% studies have unclear risk of detection bias. All studies have low risk of attrition bias and reporting bias. About 47% studies have low risk of other bias, and 53% studies have high risk of other bias.

Fig. 2
figure 2

The risk-of-bias assessments of the included studies. The risk-of-bias assessments of the included studies. Random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), selective reporting (reporting bias), and other bias were assessed by Review Manager 5.4 as “low,” “high,” or “unclear” risk. Green color indicated low risk of bias, yellow color indicated unclear risk of bias, and red color indicated high risk of bias. About 71% studies have low risk of selection bias, and 29% studies have unclear risk of selection bias. About 41% studies have low risk of performance bias, and 51% studies have unclear risk of performance bias. About 29% studies have low risk of detection bias, and 71% studies have unclear risk of detection bias. All studies have low risk of attrition bias and reporting bias. About 47% studies have low risk of other bias, and 53% studies have high risk of other bias

Main efficacy of meta-analysis

The prioritization of the inflammatory factors in infectious PTB prediction is sTNFR2 > TNFα > IL-10 > IL-6 > CRP > IL-1β

Among the included 17 studies, 1340 normal and 502 infectious PTB pregnancies were analyzed, and 19 inflammatory factors were studied in the peripheral blood of pregnant women. In the peripheral blood of normal and PTB patients, C-reactive protein (CRP), interleukins (IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-17A, IL-27p28), tumor necrosis factor (TNF)/nerve growth factor (NGF) cytokine family (TNFα, sTNFR1, sTNFR2, NGF), macrophage related soluble factors (sCD14, sCD163), interferons (IFN-γ), chemokines (CXCL9), ferritin, and intestinal fatty acid binding protein (I-FABP) were analyzed. Among 19 inflammatory factors, 6 inflammatory factors were studied more than twice, and IL-1β, IL-6, IL-10, CRP, TNFα, and sTNFR2 were included. We used network meta-analysis for these 6 inflammatory factors to efficiently rank the priority for infectious PTB prediction. The network and cumulative probabilities of these 6 inflammatory biomarkers were shown in Fig. 3A and Figure 3B respectively. The network analyzed results showed in Table 2 indicated that sTNFR2 had the highest SUCRA value (94%), TNFα had a 64.2% SUCRA value, IL-10 had a 63.9% SUCRA value, IL-6 had a 42.5% SUCRA value, CRP had a 18.9% SUCRA value, and IL-1β had the lowest SUCRA value (16.5%). Thus, the prioritization of the inflammatory factors in infectious PTB prediction is sTNFR2 > TNFα > IL-10 > IL-6 > CRP > IL-1β.

Fig. 3
figure 3

The network and cumulative probabilities of IL-1β, IL-6, IL-10, CRP, TNFα, and sTNFR2. a The network of IL-1β, IL-6, IL-10, CRP, TNFα, and sTNFR2. The size of the nodes and the thickness of the edges are weighted according to the number of studies evaluating each inflammatory factors and direct comparison, respectively. b The cumulative probabilities of IL-1β, IL-6, IL-10, CRP, TNFα, and sTNFR2 to rank the priority for PTB prediction. The curve of each inflammatory factors is the cumulative ranking curve lining out the SUCRA value. (IL-1β indicated interleukin-1 beta, IL-6 indicated interleukin-6, IL-10 indicated interleukin-10, CRP indicated C-reactive protein, TNFα indicated tumor necrosis factor α, and sTNFR2 indicated soluble tumor necrosis factor receptor 2, SUCRA indicated surface under the cumulative ranking curve)

Table 2 The SUCRA values of inflammatory factors for PTB prediction.

CRP/IL-1β/IL-6 is reliable cluster for predicting the occurrence of infectious PTB in gestational 27–34 weeks

The studies (Skrablin, S. 2007; Zhu, H. 2018) found that CRP, IL-1β and IL-6 these three inflammatory factors both upregulated in maternal blood of PTB patients in gestational 27–34 weeks comparing with normal [23, 24], and these two studies were included for further analysis. Based on this, the global consideration of multiple inflammatory factors at specific time for infectious PTB prediction is necessary. The forest plot indicated that CRP, IL-1β, and IL-6 all significantly higher in the maternal blood of PTB patients compared with normal (SMD: 3.633; 95% CI: 1.971 to 5.296; P < 0.05, and I2 = 93.5%) in gestational 27–34 weeks (Fig. 4). The funnel plot with pseudo 95% confidence limits was shown in Supplementary data 2A, and the sensitivity analysis shown in Supplementary data 2B did not significantly affect the overall results, supporting the function of CRP, IL-1β and IL-6 together as a reliable cluster for infectious PTB in gestational 27–34 weeks.

Fig. 4
figure 4

The forest plot of comparison of maternal CRP/IL-1β/IL-6 cluster between normal and PTB patients in gestational 27–34 weeks. SMD: 3.633; 95% CI: 1.971 to 5.296; P < 0.05, and I2 = 93.5%. (SMD indicated standard mean difference; CI indicated confidence interval)

TNF/NGF family is potential cluster for predicting the occurrence of infectious PTB in gestational 25–33 weeks

Signals emanating from receptors of the TNF/NGF family take part in immune defense [25]. TNF, NGF, TNF receptor, and NGF receptor all belong to the TNF/NGF family. Among 17 selected references in this study, 3 references (Escobar-Arregoces, F. 2018; Wallenstein, M.B. 2016; McDonald, C. R. 2019) that related to the TNF/NGF family and analyzed maternal blood in gestational 25–33 weeks of PTB were chosen for further analysis [21, 26, 27]. Based on these, we looked at TNFα, TNFβ, NGF, sTNFR1, and sTNFR2 in these 3 studies to see if the TNF/NGF family participated in the occurrence of infectious PTB. The forest plot result showed that the TNF/NGF family participated in infectious PTB occurrence (SMD: 1.592; 95% CI: 0.020 to 3.163; P < 0.05, and I2 = 98.4%) (Figure 5). The funnel plot with pseudo 95% confidence limits was shown in Supplementary data 3A. To show the impact of each study, we did the sensitivity analysis in supplementary data 3B. The results of sensitivity analysis showed that the three studies all contributed importantly, and excluding either study would affect the statistical difference.

Fig. 5
figure 5

The forest plot of comparison of maternal TNF/NGF family related factors between normal and PTB patients in 25–33 weeks. SMD: 1.592; 95% CI: 0.020 to 3.163; P < 0.05, and I2 = 98.4%. (SMD indicated standard mean difference; CI indicated confidence interval)

Discussion

As it became attractive that inflammatory factors can be a significant trigger for labor in pregnancy [28], the number of studies examining the interaction between inflammatory factors and infectious PTB increased dramatically. Thus, a systematic study to find the maternal inflammatory biomarkers for infectious PTB is necessary.

Actually, most of the PTB related studies are retrospective, and the blood sampling time during gestation is not continuous. Clinically, maternal blood can be obtained at around 15–20 gestational weeks for Down screening, or at PTB delivery time at around 27–37 gestational weeks. If the patient shows signs of parturition very early and undergoes hospitalization, maternal blood can also be taken before 27 weeks of gestation. These are the reasons for sampling at different gestational age of infectious PTB patients among different studies.

Based on an analysis of pooled data from the included 17 references, the overall findings from this review showed that infectious PTB was an inflammation-related disease with maternal high levels of inflammatory factors. In particularly, the prioritization of the inflammatory factors in PTB prediction is sTNFR2 > TNFα > IL-10 > IL-6 > CRP > IL-1β. TNFα is a primary mediator of systemic responses to infection. The soluble receptor forms of TNFα are sTNFR1 and sTNFR2. TNFα and the soluble receptor forms increase in inflammation both with and without infection [29,30,31]. IL-6 has opposite effect under different inflammation environment. During the chronic inflammation and autoimmunity situation, IL-6 activity can be commonly found dysregulated. However, when inflammation occurs with infection, IL-6 increases to activate innate and adaptative immune responses [32]. From our study, CRP/ IL-1β/ IL-6 during 27–34 weeks and TNF/NGF family during 25–33 weeks can be potential biomarkers for predicting the occurrence of infectious PTB at accurate gestational age (Fig. 6).

Fig. 6
figure 6

A scheme of the potential inflammatory biomarkers of infectious PTB based on the results revealed in this study. Infectious PTB was an inflammation-related disease with maternal high levels of several inflammatory factors. The prioritization of the inflammatory factors in infectious PTB prediction is sTNFR2 > TNFα > IL-10 > IL-6 > CRP > IL-1β. CRP/IL-1β/IL-6 in gestational 27–34 weeks, and TNF/NGF family in gestational 25–33 weeks, were potential biomarker clusters that specific for infectious PTB prediction. (IL-1β indicated interleukin-1 beta, IL-2 indicated interleukin-2, IL-4 indicated interleukin-4, IL-6 indicated interleukin-6, IL-8 indicated interleukin-8, IL-10 indicated interleukin-10, IL-17A indicated interleukin-17A, IL-27p28 indicated interleukin-27p28, CRP indicated C-reactive protein, TNFα indicated tumor necrosis factor α, sTNFR indicated soluble tumor necrosis factor receptor, NGF indicated nerve growth factor, CXCL9 indicated C-X-C motif chemokine ligand 9, IFN-γ indicated interferon gamma, sCD14 indicated soluble CD14 molecule, and sCD163 indicated soluble CD163 molecule)

During normal pregnancy, the healthy growth of the semi-allogeneic fetus requires the adaptive regulation of the maternal immune system to establish an immune-tolerant environment [33]. In addition, disruption of immune regulation at the maternal–fetal interface is tightly associated with various pregnancy-related diseases [34]. Decidual natural killer cells and macrophages have a key role in immune regulation at the maternal–fetal interface with the function of secreting interleukins [35,36,37]. Abnormal levels of IL-6, which is secreted by decidual natural killer cells [38], and IL-1β, which is secreted by macrophages [39], were found in pregnancy disorders. In addition, elevated CRP levels are also found with chronic placental inflammation. In this way, the abnormal fluctuations of the inflammatory factors, such as CRP, IL-1β, and IL-6, can reflect pregnancy disorders to some extent.

CRP, IL-1β, and IL-6 are also significantly increased in preeclamptic patients [40], so the inflammatory biomarkers for distinct different pregnancy-related diseases, such as PTB and preeclampsia, need further study. In preeclampsia, a systematic study found that CRP significantly upregulated in maternal blood in third trimester, IL-1β could be potential biomarkers in first trimester, and IL-6 upregulated in second and third trimesters [40]. In our study, we systematically found that CRP, IL-1β, and IL-6 all significantly upregulated in gestational 27–34 weeks in maternal blood of infectious PTB patients. Based on these, the expression patterns of CRP, IL-1β, and IL-6 between infectious PTB and preeclamptic maternal blood are different. CRP/IL-1β/IL-6 can be reliable cluster for infectious PTB prediction during gestational 27–34 weeks. This cluster can be special biomarker for infectious PTB, and helps to distinguished from preeclampsia.

The TNF/NGF family was first pointed out to be good candidates for infectious PTB prediction in our study, and our result indicated that the global consideration of multiple inflammatory factors could be a new direction for infectious PTB biomarkers in future studies. In this way, taking more than one biomarker into consideration for different prenatal disorders is necessary.

Limitations

There was inevitable heterogeneity in the studies that we chose to analyze. The study population, age, parity, gestational age, and method of statistics have variations among different studies. Therefore, it is important to interpret the study results with caution. In addition, it will be better to find the biomarkers as early as possible to predict infectious PTB. However, few studies analyzed the inflammatory factors in maternal blood in the first trimester. In this way, the inflammatory biomarkers that were found in this study are much more reliable after the first trimester of pregnancy.

Conclusion

Our study made a network meta-analysis by using the principle of the PICOS framework according to the recommendations of PRISMA to guarantee objectivity. We confirmed that infectious PTB was an inflammation-related disease with maternal high levels of inflammatory factors. In particularly, the prioritization of the inflammatory factors in infectious PTB prediction is sTNFR2 > TNFα > IL-10 > IL-6 > CRP > IL-1β. Furthermore, our innovative findings indicated that global consideration of multiple inflammatory factors, such as CRP/IL-1β/IL-6 biomarker cluster in gestational 27–34 weeks, and the TNF/NGF family in gestational 25–33 weeks, were potential biomarker clusters that specific for infectious PTB prediction. This study systematically provided evidence to both rank the inflammatory biomarkers and find several maternal inflammatory clusters at accurate gestational week for infectious PTB prediction. The global consideration of multiple inflammatory factors at accurate gestational age is highlighted.

Availability of data and materials

The data of this study are available from the corresponding author upon reasonable request.

Abbreviations

PTB:

Preterm birth

IL:

Interleukin

CRP:

C-reactive protein

TNF:

Tumor necrosis factor

sTNFR:

Soluble tumor necrosis factor receptor

NGF:

Nerve growth factor

IFN:

Interferon

I-FABP:

Intestinal fatty acid binding protein

CXCL9:

C-X-C motif chemokine ligand 9

IFN-γ:

Interferon gamma

sCD14:

Indicated soluble CD14 molecule

sCD163:

Soluble CD163 molecule

SUCRA:

Surface under the cumulative ranking curve

PICOS:

Intervention, comparison, outcome, and study design

PRISMA:

Preferred reporting items for systematic reviews and meta-analysis

SMD:

Standard mean difference

CI:

Confidence interval

References

  1. Rubens CE, et al. Global report on preterm birth and stillbirth (7 of 7): mobilizing resources to accelerate innovative solutions (Global Action Agenda). BMC Pregnancy Childbirth. 2010;10(Suppl 1):S7.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Martin JA, et al. Births: Final Data for 2015. Natl Vital Stat Rep. 2017;66(1):1.

    PubMed  Google Scholar 

  3. Chawanpaiboon S, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health. 2019;7(1):e37–46.

    Article  PubMed  Google Scholar 

  4. Romero R, Dey SK, Fisher SJ. Preterm labor: one syndrome, many causes. Science. 2014;345(6198):760–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Goldenberg RL, et al. Epidemiology and causes of preterm birth. Lancet. 2008;371(9606):75–84.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Munoz-Perez VM, et al. Preterm Birth, Inflammation and Infection: New Alternative Strategies for their Prevention. Curr Pharm Biotechnol. 2019;20(5):354–65.

    Article  CAS  PubMed  Google Scholar 

  7. Agrawal V, Hirsch E. Intrauterine infection and preterm labor. Semin Fetal Neonatal Med. 2012;17(1):12–9.

    Article  PubMed  Google Scholar 

  8. Elovitz MA, et al. A new model for inflammation-induced preterm birth: the role of platelet-activating factor and Toll-like receptor-4. Am J Pathol. 2003;163(5):2103–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Romero R, et al. The role of infection in preterm labour and delivery. Paediatr Perinat Epidemiol. 2001;15(Suppl 2):41–56.

    Article  PubMed  Google Scholar 

  10. Kannan S, et al. Dendrimer-based postnatal therapy for neuroinflammation and cerebral palsy in a rabbit model. Sci Transl Med. 2012;4(130):130–46.

    Article  Google Scholar 

  11. Petelin M, et al. Systemic up-regulation of sTNFR2 and IL-6 in Porphyromonas gingivalis pneumonia in mice. Exp Mol Pathol. 2004;76(1):76–81.

    Article  CAS  PubMed  Google Scholar 

  12. Christiaens I, et al. Inflammatory processes in preterm and term parturition. J Reprod Immunol. 2008;79(1):50–7.

    Article  CAS  PubMed  Google Scholar 

  13. Hirsch E, Saotome I, Hirsh D. A model of intrauterine infection and preterm delivery in mice. Am J Obstet Gynecol. 1995;172(5):1598–603.

    Article  CAS  PubMed  Google Scholar 

  14. Pandey M, Chauhan M, Awasthi S. Interplay of cytokines in preterm birth. Indian J Med Res. 2017;146(3):316–27.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Garcia-Ruiz G, et al. In vitro progesterone modulation on bacterial endotoxin-induced production of IL-1beta, TNFalpha, IL-6, IL-8, IL-10, MIP-1alpha, and MMP-9 in pre-labor human term placenta. Reprod Biol Endocrinol. 2015;13:115.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lu Q, et al. Changes of serum IL-10, IL-1beta, IL-6, MCP-1, TNF-alpha, IP-10 and IL-4 in COVID-19 patients. Int J Clin Pract. 2021;75(9): e14462.

    Article  CAS  PubMed  Google Scholar 

  17. Moutsopoulos NM, Madianos PN. Low-grade inflammation in chronic infectious diseases: paradigm of periodontal infections. Ann N Y Acad Sci. 2006;1088:251–64.

    Article  CAS  PubMed  Google Scholar 

  18. Sproston NR, Ashworth JJ. Role of C-reactive protein at sites of inflammation and infection. Front Immunol. 2018;9:754.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Hastie CE, et al. Association between preterm delivery and subsequent C-reactive protein: a retrospective cohort study. Am J Obstet Gynecol. 2011;205(6):556-e1-4.

    Article  Google Scholar 

  20. Gupta JK, et al. Publisher Correction: Genome and transcriptome profiling of spontaneous preterm birth phenotypes. Sci Rep. 2022;12(1):1986.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. McDonald CR, et al. Systemic inflammation is associated with malaria and preterm birth in women living with HIV on antiretrovirals and co-trimoxazole. Sci Rep. 2019;9(1):6758.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Shamseer L, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ. 2015;350: g7647.

    Article  PubMed  Google Scholar 

  23. Zhu H, Yang MJ. Maternal plasma concentrations of macrophage migration inhibitory factor at first trimester as a predictive biomarker of preterm delivery in Chinese women. Clin Chim Acta. 2018;483:286–90.

    Article  CAS  PubMed  Google Scholar 

  24. Skrablin S, et al. Maternal plasma interleukin-6, interleukin-1beta and C-reactive protein as indicators of tocolysis failure and neonatal outcome after preterm delivery. J Matern Fetal Neonatal Med. 2007;20(4):335–41.

    Article  CAS  PubMed  Google Scholar 

  25. Wallach D, et al. How are the regulators regulated? The search for mechanisms that impose specificity on induction of cell death and NF-kappaB activation by members of the TNF/NGF receptor family. Arthritis Res. 2002;4(Suppl 3):S189–96.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Escobar-Arregoces F, et al. Inflamatory response in pregnant women with high risk of preterm delivery and its relationship with periodontal disease: a pilot study. Acta Odontol Latinoam. 2018;31(1):53–7.

    PubMed  Google Scholar 

  27. Wallenstein MB, et al. Inflammatory biomarkers and spontaneous preterm birth among obese women. J Matern Fetal Neonatal Med. 2016;29(20):3317–22.

    CAS  PubMed  Google Scholar 

  28. Romero R, et al. Interleukin-1 alpha and interleukin-1 beta in preterm and term human parturition. Am J Reprod Immunol. 1992;27(3–4):117–23.

    Article  CAS  PubMed  Google Scholar 

  29. Keller CR, et al. Kidney function and markers of inflammation in elderly persons without chronic kidney disease: the health, aging, and body composition study. Kidney Int. 2007;71(3):239–44.

    Article  CAS  PubMed  Google Scholar 

  30. Shiau S, et al. Unique profile of inflammation and immune activation in pregnant people with HIV in the United States. J Infect Dis. 2023;227(5):720–30.

    Article  CAS  PubMed  Google Scholar 

  31. Erlandson KM, et al. The impact of moderate or high-intensity combined exercise on systemic inflammation among older persons with and without HIV. J Infect Dis. 2021;223(7):1161–70.

    Article  CAS  PubMed  Google Scholar 

  32. Villar-Fincheira P, et al. Role of interleukin-6 in vascular health and disease. Front Mol Biosci. 2021;8: 641734.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yang F, Zheng Q, Jin L. Dynamic Function and Composition Changes of Immune Cells During Normal and Pathological Pregnancy at the Maternal-Fetal Interface. Front Immunol. 2019;10:2317.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Erlebacher A. Immunology of the maternal-fetal interface. Annu Rev Immunol. 2013;31:387–411.

    Article  CAS  PubMed  Google Scholar 

  35. Pitman H, et al. Altered expression of interleukin-6, interleukin-8 and their receptors in decidua of women with sporadic miscarriage. Hum Reprod. 2013;28(8):2075–86.

    Article  CAS  PubMed  Google Scholar 

  36. Saito S, et al. Cytokine production by CD16-CD56bright natural killer cells in the human early pregnancy decidua. Int Immunol. 1993;5(5):559–63.

    Article  CAS  PubMed  Google Scholar 

  37. Lash GE, et al. Decidual macrophages: key regulators of vascular remodeling in human pregnancy. J Leukoc Biol. 2016;100(2):315–25.

    Article  CAS  PubMed  Google Scholar 

  38. Carp H. Cytokines in recurrent miscarriage. Lupus. 2004;13(9):630–4.

    Article  CAS  PubMed  Google Scholar 

  39. Li ZH, et al. Galectin-9 alleviates LPS-induced preeclampsia-like impairment in rats via switching decidual macrophage polarization to M2 subtype. Front Immunol. 2018;9:3142.

    Article  CAS  PubMed  Google Scholar 

  40. Black KD, Horowitz JA. Inflammatory markers and preeclampsia: a systematic review. Nurs Res. 2018;67(3):242–51.

    Article  PubMed  Google Scholar 

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Acknowledgements

We acknowledge professor Qiaoying Zhang and Guangyao Fan in Shaoxing University for the language support.

Funding

Research reported in this publication was supported by the National Natural Science Foundation of China (No. 82201866 to Yeling Ma), 2022 general scientific research projects of Zhejiang Provincial Department of Education (No. Y202248567 to Yeling Ma), Startup Fund for Research of Shaoxing University (No. 20210022 to Yeling Ma), the National Natural Science Foundation (No. 82101766 to Qian Yang), and the Shanghai Sailing Program (21YF1439300 to Qian Yang).

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Jiayi Li, Jianrong Ge and Danan Fang searched the database, extracted the data, and assess the quality of the included studies. Jiayi Li, Na Ran, Changwu Zheng, and Yumeng Fang draw the figures, drafts the manuscripts and collects the references. Jiayi Li and Qian Yang modified the language and revised the manuscript. Yeling Ma designed and supervised the study, and revised the manuscript.

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Correspondence to Yeling Ma.

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Supplementary information

Additional file 1: Supplementary data 1.

Research terms of this study. Supplementary data 2. The funnel plot and sensitivity analysis of maternal CRP, IL-1β and IL-6 biomarker clusters between normal and PTB. Supplementary data 3. The funnel plot and sensitivity analysis of maternal TNF/NGF family related molecules between normal and PTB.

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Li, J., Ge, J., Ran, N. et al. Finding the priority and cluster of inflammatory biomarkers for infectious preterm birth: a systematic review. J Inflamm 20, 25 (2023). https://doi.org/10.1186/s12950-023-00351-0

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Keywords

  • Preterm birth
  • Inflammatory factor
  • Biomarker
  • Prioritization
  • Maternal blood