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. 2021 Nov 18;16(11):e0260115. doi: 10.1371/journal.pone.0260115

Prostaglandins in biofluids in pregnancy and labour: A systematic review

Eilidh M Wood 1, Kylie K Hornaday 1, Donna M Slater 1,2,*
Editor: Tamas Zakar3
PMCID: PMC8601582  PMID: 34793529

Abstract

Prostaglandins are thought to be important mediators in the initiation of human labour, however the evidence supporting this is not entirely clear. Determining how, and which, prostaglandins change during pregnancy and labour may provide insight into mechanisms governing labour initiation and the potential to predict timing of labour onset. The current study systematically searched the existing scientific literature to determine how biofluid levels of prostaglandins change throughout pregnancy before and during labour, and whether prostaglandins and/or their metabolites may be useful for prediction of labour. The databases EMBASE and MEDLINE were searched for English-language articles on prostaglandins measured in plasma, serum, amniotic fluid, or urine during pregnancy and/or spontaneous labour. Studies were assessed for quality and risk of bias and a qualitative summary of included studies was generated. Our review identified 83 studies published between 1968–2021 that met the inclusion criteria. As measured in amniotic fluid, levels of PGE2, along with PGF2α and its metabolite 13,14-dihydro-15-keto-PGF2α were reported higher in labour compared to non-labour. In blood, only 13,14-dihydro-15-keto-PGF2α was reported higher in labour. Additionally, PGF2α, PGF1α, and PGE2 were reported to increase in amniotic fluid as pregnancy progressed, though this pattern was not consistent in plasma. Overall, the evidence supporting changes in prostaglandin levels in these biofluids remains unclear. An important limitation is the lack of data on the complexity of the prostaglandin pathway outside of the PGE and PGF families. Future studies using new methodologies capable of co-assessing multiple prostaglandins and metabolites, in large, well-defined populations, will help provide more insight as to the identification of exactly which prostaglandins and/or metabolites consistently change with labour. Revisiting and revising our understanding of the prostaglandins may provide better targets for clinical monitoring of pregnancies. This study was supported by the Canadian Institutes of Health Research.

Introduction

It is widely believed that prostaglandins are important in the initiation of human labour [1]. Multiple studies have documented increased expression of cyclooxygenases, key enzymes in prostaglandin synthesis, in gestational tissues with the onset of labour, however, this has not been consistently observed [2]. Additionally, prostaglandins are present in maternal blood, urine, and amniotic fluid during pregnancy [3], however, the evidence supporting or refuting their role in labour is conflicting. Prostaglandins are known to affect uterine contractility and cervical ripening [4] and have thus been successfully used for labour induction since the late 1960’s, though the use of prostaglandin synthesis inhibitors for prevention of preterm birth has been minimally successful and is associated with various fetal side effects [5]. Since their discovery in the 1930s, prostaglandins and their synthesis and metabolism are now known to be highly complex, which may contribute to these inconsistent outcomes seen during clinical targeting of this pathway. Aside from providing insight into labour processes, the presence of prostaglandins in peripheral tissues offers the potential for minimally invasive early prediction of labour onset and the ability to distinguish between true and false labour, which remains an ongoing clinical challenge [6]. Additionally, it has been suggested that biomarkers predictive of term labour (>37 weeks gestation) may also be useful for prediction of preterm labour (<37 weeks gestation), as both processes share common physiological changes involving cervical ripening, uterine contractions, and membrane rupture [7]. In 2010, preterm birth was estimated to occur in approximately 11% of all pregnancies and remains the leading cause of neonatal mortality worldwide [8], yet there is a lack of objective measures available to assess risk of premature delivery. Accurate prediction of term and preterm labour would allow for more informed patient planning and more efficient use of healthcare resources, for example, by reducing unnecessary hospitalizations and interventions. Despite evidence to suggest a role for prostaglandins in pregnancy and labour, literature defining the complexities of the pathway remain inconclusive and inconsistent. Therefore, we have systematically reviewed the scientific literature with the aim of answering three main questions to find evidence that either supports or refutes a role for prostaglandins in the initiation of labour: 1) Are prostaglandins or their metabolites detectable in biofluids in higher amounts in labour vs not in labour? 2) Are prostaglandins or their metabolites detected in increasing amounts prior to the onset of labour? And 3) Are prostaglandins or their metabolites present in urine, blood, or amniotic fluid predictive of preterm labour?

Methods

This systematic review was conducted and reported following the recommendations of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The protocol is available upon request. This review was not registered.

Information sources

The databases MEDLINE and EMBASE were searched for records. Additionally, the reference lists of eligible studies and relevant review articles were manually searched.

Search strategy

The search strategy included the key words “prostaglandins” AND “obstetric labor” AND (“amniotic fluid” OR “blood” OR “urine”) as well as synonyms, related alternatives, and Medical Subject Heading (MeSH) terms as relevant. The searches were limited to human studies. Full details of the search terms for each database are given in S1 and S2 Tables. Citations retrieved from the initial search were downloaded into a reference manager (EndNote X9) and duplicates were removed. Two reviewers (EW and SLW) independently reviewed abstracts and removed those not relevant to the research questions. Following retrieval of full-text articles, both reviewers assessed the remaining citations against the eligibility criteria. Studies excluded at this level were sorted based on reason for exclusion. Disagreements were resolved by discussion until consensus was reached.

Inclusion/Exclusion criteria

Primary study journal articles examining endogenous prostaglandins in blood, amniotic fluid, and/or urine during pregnancy and spontaneous labour were included in this review. Studies were excluded if the study was on animals, the study was examining exogenous prostaglandins for induction of labour or if participants experienced spontaneous abortion (prior to 20 weeks). As well, studies which only had samples collected following delivery were excluded. Publications with incomplete information (i.e., conference abstracts) were excluded. Only studies written in English or with an available English translation were included. The search did not include a time restriction, however, the databases MEDLINE and EMBASE include literature published since 1946 and 1947 respectively. The search was initially conducted on May 19, 2020 and was repeated on August 20, 2021.

Selection process/Data extraction

The following information was extracted by one reviewer (EW) from each of the final selected studies: population examined, sample number, type of biofluid collected, method of testing and measurement, metabolites/prostaglandins measured, time of sample collection, country of study origin, available measures of central tendency and variance, and major findings of the study.

Quality assessment

Studies were assessed for quality and risk of bias using a quality assessment tool (Table 1) adapted from Hadley et al. [9] for assessment of basic science research. Full details of the rubric can be found in Table 1. Studies were scored between 0–9. All studies were scored independently by two investigators (EW and KH) and disagreements in scores were resolved by discussion.

Table 1. Quality assessment rubric.

Quality assessment 1 point 0 points N/A
1) Question/objective sufficiently described? Primary study question or objective is clearly stated Unclear question/objective or no question/objective
2) Design appropriate to answer study question? Study design is clearly stated and makes sense according to the study question/objective E.g. uses convenient samples or study does not give enough information to determine study design
3) Methods described in sufficient detail to allow for study to be replicated? Samples, reagents, assay used to measure prostaglandins are sufficiently described, methods for sample collection are clearly described Some information missing or no information/ insufficient information is given on samples, reagents, assays, methods for sample collection
4) Researchers used blinding? Yes No
5) Sample number sufficient for internal validity? Study has pre-planned sample size and/or power analysis or confidence intervals suggest sufficient sample size No power analysis or confidence intervals suggest insufficient sample size
6) Appropriate negative controls? Control group is appropriate to answer study question Controls are from a clearly different population
7) Appropriate statistical analysis? There is a comparison of means with appropriate transformations of data No statistical analysis provided
8) Results reported in sufficient detail? Results match methods i.e. all prostaglandins measured are reported on Some measurements missing from results
9) Do the results support the conclusion? Conclusion makes sense given results and answers primary study question/objective Conclusion is overstated based on results or not related to main study question and main results
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Data synthesis

A qualitative summary was generated, and tables were created with the main results from each study. No pooled analysis was performed.

Results

Studies identified

The electronic search returned 2257 unique records after removal of duplicates from 2688 records. 2101 records were removed at the title/abstract level, leaving 156 records for assessment at the full-text level. Hand search of reference lists yielded an additional 35 records for review, resulting in a total of 191 full text records. Of the records assessed at the full text level, 108 were excluded, leaving n = 83 studies for inclusion in this review (Fig 1).

Fig 1. PRISMA diagram.

Fig 1

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Abbreviations: PG = prostaglandin, IOL = induction of labour.

Main characteristics of studies

Summaries of the main characteristics and relevant findings of the included studies can be found in Table 2 (presented in chronological order). Of the 83 studies, most assessed only one biofluid, (34 plasma, 32 amniotic fluid, 8 urine, 4 serum) while 6 studies assessed multiple biofluids. The range of prostaglandins and metabolites investigated included PGF2α, PGF1α, 13,14-dihydro-15-keto-PGF2α (PGFM), 5α,7α-dihydroxy-11-keto-tetranor-prostane-1,16-dioic acid (t-PGFM), PGE1, PGE2, 15-keto-PGE2, 13,14-dihydro-15-keto-PGE2 (PGEM), 11-deoxy-13,14-dihydro-15-keto-11,16-bicyclo-PGE2 (bicyclo-PGEM), 6-keto-PGF1α, 2,3-dinor-6-keto-PGF1α, PGA2, PGD2, PGJ2, 19-OH-PGE2, and 9α,11β-PGF2. Prostaglandins and the corresponding metabolites measured are described in Fig 2. In addition, many older studies used measurement techniques which were unable to differentiate between subcategories of prostaglandins and therefore reported levels of PGE or PGF. The range of prostaglandin concentrations reported using different measurement techniques are shown in Table 3.

Table 2. Main characteristics of studies.

Study Method Sample Size Biofluid PG/Metabolite Relevant Findings
Karim 1968 [10] TLC and biological assay n = 42 NL, n = 10 TL plasma PGF2α, PGF1α, PGE1, PGE2 NL < LOD
PGF2α higher at delivery than 1st stage labour
Brummer 1972 [11] RIA n = 40 NL, n = 46 L serum PGF2α L>TNL
late pregnancy similar to nonpregnant
increased through 1st stage labour (early-late), then decreased in 2nd stage
Gutierrez-Cernosek & Levine 1972 [12] RIA n = 10 1st TM serum PGF2α peaked at 2nd TM, decreased to nonpregnant levels at term
n = 52 2nd TM
n = 54 3rd TM
n = 9 serial (14-40wks)
Brummer 1973 [13] unknown n = 13 1st TM serum PGF2α decreased in 2nd TM, plateaued in 3rd TM
n = 40 2nd TM
n = 75 3rd TM
Brummer & Craft 1973 [14] RIA n = 58 L serum PGF2α highest in 1st stage labour, decreased in 2nd stage and remained low
n = 7 serial L
Hertelendy et al 1973 [15] RIA n = 8 PTNL plasma PGE <32wks pregnant similar to nonpregnant
n = 32 L increased through 1st stage labour (early-late), then decreased in 2nd stage
Keirse & Turnbull 1973 [16] GC n = 12 TNL, n = 38 TL AF PGE2 TL>TNL
increased through 1st stage labour
PGE1 <LOD
Salmon & Army 1973 [17] RIA n = 57 AF PGF2α L>TNL
constant through 2nd and 3rd TM, rise after 36wks
spike during 1st stage labour
Challis et al 1974 [18] RIA n = 4 TNL, n = 9 TL plasma PGF TL>TNL (nonsignificant)
Green et al 1974 [19] GC-MS n = 2 TL plasma PGF2α no correlation with stage of labour
n = 5 term serial PGFM TL>TNL
increased through 1st stage labour
Hamberg 1974 [20] RIA? n = 3 serial (9-40wks) urine t-PGFM increased with GA, peaked at term
n = 8 TNL, n = 1TL TL>TNL
Hennam et al 1974 [21] RIA n = 13 1st TM plasma PGF2α levels lowest at 2nd TM compared to 1st and 3rd
n = 10 2nd TM L>3rd TM
n = 20 3rd TM
n = 99 L
Hibbard et al 1974 [22] RIA n = 42 TNL, n = 13 TL AF PGF2α TL>TNL
increased with GA after 36 weeks
n = 22 PTNL 64% <LOD
Hillier et al 1974 [23] RIA n = 11 TNL, n = 5 TL AF PGF2α TL>TNL
increased with labour stage
plasma no correlation with labour stage
Keirse et al 1974 [24] RIA and GC n = 20 TNL, n = 26 TL AF PGF TL>TNL
n = 8 PTNL TNL>PTNL
increased through 1st stage labour
MacDonald et al 1974 [25] RIA n = 6 NL, n = 6 L AF PGF2α L>NL
Singh & Zuspan 1974 [26] PC and TD n = 6 AF PGF2α, PGF1α, PGE1, PGE2 constant from 24-36wks, increase in labour
Hillier et al 1975 [27] RIA n = 13 TNL, n =? TL AF PGF increased with labour stage, peaked before delivery
n = 8 PTNL increased from 2nd TM to term
Johnson et al 1975 [28] RIA n = 38 NL, n = 8 L AF PGF2α L>NL
n = 11 PTNL 3rd TM > 2nd TM
n = 33 NL, n = 99 L plasma no difference between NL and L
n = 15 PTNL no pattern with labour
Pokoly & Jordan 1975 [29] RIA n = 6 TNL, n = 2 TL (CS) AF PGF TL>TNL for CS only
n = 4 TNL, n = 13 TL plasma no difference between NL and L
AF PGE TL>TNL (nonsignificant)
plasma no difference between NL and L
Dray & Frydman 1976 [30] RIA n = 24 NL, n = 37 L AF PGF2α L>NL
n = 19 PTNL higher in late 3rd TM than early 3rd TM
increased with labour stage
PGE2 L>TNL
<LOD before 24wks, increased to 36wks, then remained constant to term
increased with labour stage
PGE1 <LOD
Granstrom & Kindahl 1976 [31] RIA n = 1 term serial urine t-PGFM TL>TNL
late 3rd TM > nonpregnant
Keirse et al 1977 [32] RIA n = 40 TNL, n = 46 TL AF PGF, PGFM TL>TNL
increased through 1st stage labour
Kinoshita et al 1977 [33] RIA n = 7 TNL, n = 10 TL AF PGF2α TL>TNL
PGE1 no difference between TNL and TL
n = 10 TL, n = 10 TNL plasma PGF2α no difference between TNL and TL
n = 10 3rd TM serial no pattern with gestation in 3rd TM
PGE1 no difference between TNL and TL
no pattern with gestation in 3rd TM
no correlation with labour stage
TambyRaja et al 1977 [34] RIA n = 27 PTL AF PGF2α increased through 1st stage labour
Haning et al 1978 [35] RIA n = 4 TNL, n = 8 TL plasma PGFM TL>TNL
Mitchell et al 1978 [36] RIA n = 13 NL, n = 10 L plasma PGF, PGE L > NL
n = 7 PTL no correlation with stage of labour
PGFM L > NL
no difference with PTL and NL
increased with labour stage
Nieder & Augustin 1978 [37] RIA n = 34 AF PGF2α, PGE increased from 31wks to term, steeper after 36wks
plasma no correlation with GA
Zuckerman et al 1978 [38] RIA n = 5 L plasma PGF2α lower in 1st stage labour than 2nd or 3rd
peaked at delivery and at placental separation
Ghodaonkar et al 1979 [39] RIA n = 2 serial (20-40wks) plasma PGFM no pattern with gestation
n = 14 TL increased in 2nd and 3rd stages of labour
Mitchell et al 1979 [40] RIA n = 24 NL, n = 31 TL AF 6-keto-PGF1a TL>TNL
no correlation with GA or cervical dilation
Satoh et al 1979 [41] RIA n = 17 serial (8-39wks) AF PGFM TL>TNL
no pattern with gestation in 3rd TM
n = 8 TNL, n = 10 TL plasma TL>TNL
n = 53 PTNL no correlation with GA
increased with labour stage, peaked at delivery
n = 30 3rd TM serial urine t-PGFM L>NL
Lewis et al 1980 [42] GC-MS n = 6 1st TM plasma 6-keto-PGF1a 2nd-3rd TM > nonpregnant
n = 9 2nd-3rd TM
Dubin et al 1981 [43] RIA n = 39 serial (16-40wks) plasma PGFM TL>TNL
n = 17 PTD no correlation with GA
Sellers et al 1981 [44] RIA n = 13 TNL, n = 21 TL plasma PGFM TL>TNL
n = 12 PTNL, n = 22 PTL PTL>PTNL
no difference between PTNL and PTL
no difference between PTL who delivered term and preterm
increased with labour stage in PTL and TL
Ylikorkala et al 1981 [45] RIA n = 9 serial plasma 6-keto-PGF1a TL>TNL
increased with labour stage
Fuchs et al 1982 [46] RIA n = 14 TNL, n = 20 TL plasma PGFM TL>TNL
Fuchs et al 1982 [47] RIA n = 10 TNL, n = 14 TL plasma PGFM TNL>PTNL
n = 10 PTNL, n = 15 PTL PTL>PTNL
Mitchell et al 1982 [48] RIA n = 10 TNL, n = 10 TL plasma bicyclo-PGEM TL>TNL
n = 10 1st TM 1st TM > nonpregnant
n = 10 2nd TM decreased in 3rd TM until labour
n = 10 3rd TM
Sellers et al 1982 [49] RIA n = 10 TL plasma PGFM increased with labour stage, peaked 5min after delivery
Sharma et al 1982 [50] RIA n = 92 NL, n = 6 TL plasma PGF2α TL>NL
remained unchanged until 2wks before delivery, then increased
PGE2 no difference between TL and NL
remained unchanged until 2wks before delivery, then increased
Fuchs et al 1983 [51] RIA? n = 4 TNL, n = 17 L plasma PGFM TL>TNL
increased with labour stage
Nieder & Augustin 1983 [52] RIA n = 23 1st TM AF PGF, PGE unchanged from 9-34wks, increase at 35wks
n = 37 2nd TM
n = 103 3rd TM
Spitz et al 1983 [53] RIA n = 12 serial (10-40wks) plasma 6-keto-PGF1a decrease after 33wks
Husslein & Sinzinger 1984 [54] RIA n = 5 TNL, n = 5 TL plasma PGEM TL>TNL
n = 5 PTNL no correlation with labour stage
Nagata et al 1984 [55] RIA n = 6 term serial plasma PGF2α TL>TNL
PGE1, PGE2 no difference between NL and L
no correlation with labour stage
Reddi et al 1984 [56] RIA n = 10 TL AF PGF, PGFM increased through 1st stage labour
Sellers et al 1984 [57] RIA n = 14 TNL, n = 9 TL plasma PGFM TL>TNL
Yamaguchi & Mori 1984 [58] RIA n = 4 <20wks plasma PGFM L>NL
n = 3 20-30wks no correlation with GA
n = 16 30-40wks 6-keto-PGF1a L>NL (nonsignificant)
Brennecke et al 1985 [59] RIA n = 9 TNL, n = 27 TL plasma PGFM TL>TNL
n = 12 serial increased with labour stage
bicyclo-PGEM no difference between TNL and TL
no correlation with GA or labour stage
Ogino & Jimbo 1986 [60] RIA n = 5 24-28wks plasma PGF2α peak at 32-36wks
n = 4 28-32wks PGE2 lowest at 36-40wks
n = 7 32-36wks
n = 8 36-40wks
Weitz et al 1986 [61] RIA n = 6 PTL-TD plasma PGFM PTL>PTNL
n = 14 PTL-PTD higher in PTL who delivered PT than those who delivered term
n = 11 PTNL
Ylikorkala et al 1986 [62] RIA n = 8 TNL, n = 13 TL urine 6-keto-PGF1a increased with labour stage and with C-section
Berryman et al 1987 [63] RIA n = 23 L AF PGD2 increased through 1st stage labour
Nagata et al 1987 [64] RIA n = 9 TL plasma PGFM increased with labour stage (nonsignificant)
PGE1 low throughout labour
Nagata et al 1987 [65] RIA n = 7 serial plasma PGFM TL>TNL
decreased 2wks prior to labour
increased with labour stage
Romero et al 1987 [66] RIA n = 23 PTNL, n = 30 PTL AF PGF2α, PGE2 PTL>PTNL
Noort et al 1988 [67] RIA n = 12 1st TM urine 6-keto-PGF1a L>NL (nonsignificant)
n = 12 2nd TM
n = 12 3rd TM
n = 12 TL
Romero et al 1988 [68] RIA n = 32 PTL-TD n = 22 PTL-PTD AF PGE2 higher in PTL who did not respond to tocolysis than those who responded to tocolysis
Sahmay et al 1988 [69] RIA n = 8 TNL, n = 9 TL AF PGF2α no difference between TNL and TL
plasma TL>TNL
AF PGE no difference between TNL and TL
plasma TNL>TL
Noort et al 1989 [70] RIA n = 7 TL plasma PGFM increased with labour stage
6-keto-PGF1a no correlation with labour stage
Romero et al 1989 [71] RIA n = 25 PTL-TD AF PGF2α no difference between PTL who delivered term and preterm
n = 16 PTL-PTD PGFM, bicyclo-PGEM higher in PTL who delivered PT than those who responded to tocolysis
Yamamoto & Kitao 1989 [72] RIA n = 76 term serial plasma PGF2α TL>TNL
increased with labour stage and delivery
Mazor et al 1990 [73] RIA n = 10 PTL-TD AF PGF2α no difference between PTL who delivered term and preterm
n = 10 PTL-PTD PGE2 higher in PTL who delivered PT than those who delivered at term
Norman & Reddi 1990 [74] RIA n = 54 TL AF PGF2α, PGFM, PGE2 increased through 1st stage labour
Fairlie et al 1993 [75] RIA n = 20 TL plasma PGFM increased with labour stage
bicyclo-PGEM in nulliparous: rose after amniotomy but did not change with labour
in multiparous: rose with amniotomy then increased with labour stage
Hillier et al 1993 [76] RIA n = 50 PTL AF PGE2 high levels associated with PTD and delivery within 1wk of amniocentesis
Johnston et al 1993 [77] RIA n = 18 TNL, n = 28 TL plasma PGFM TL>TNL
rose following amniotomy, then remained constant until delivery
PGEM TL>TNL only in primigravid
rose 1–2 after amniotomy, then remained constant until delivery
MacDonald & Casey 1993 [78] RIA n = 50 TNL, n = 190 TL AF PGF2α TL>TNL (forebag and upper compartment)
increased with labour stage, then decreased at 3-5cm dilation
PGFM TL>TNL (forebag and upper compartment)
increased with labour stage, then leveled out at 4–5.5cm dilation until delivery
PGE2 TL>TNL (forebag)
no difference between TL and TNL in upper compartment
increased with labour stage, then leveled out at 4–5.5cm dilation until delivery
Romero et al 1993 [79] RIA n = 24 NL, n = 16 TL AF PGF2α, PGFM, PGE2, 6-keto-PGF1α TL>NL
Romero et al 1994 [80] RIA n = 82 TNL, n = 168 TL AF PGF2α, PGFM, PGE2, 6-keto-PGF1α TL>TNL
increased through 1st stage labour
Lindsay et al 1995 [81] ELISA n = 8 serial (1st-3rd TM) urine 2,3-dinor-6-keto-PGF1α no correlation with GA
pregnant >> nonpregnant
Romero et al 1996 [82] RIA n = 28 serial (n = 17 L) AF PGF2α, PGE2 TL>TNL
increased with GA at term
Ichikawa & Minami 1999 [83] RIA n = 30 serial urine PGF2α TL>NL
increased from 28-36wks
PGFM TL>NL
increased from 28-36wks and again at 2nd stage of labour
Mitchell et al 2005 [84] ELISA n = 24 TNL, n = 37 TL AF 9α,11β-PGF2 TL>TNL
n = 13 PTNL, n = 56 PTL PTNL>PTL
Lee et al 2008 [85] ELISA n = 68 TNL, n = 34 TL AF PGF2α TL>TNL
n = 65 PTNL no correlation with GA until 36wks, 25-fold increase at TNL
increased with labour stage
PGE2 no difference between TL and TNL
no correlation with GA until 36wks, 2-fold increase at TNL
Lee et al 2009 [86] ELISA n = 140 PPROM (n = 126 PTD) AF PGF2α high levels associated with low GA at delivery and PTD
Maddipati et al 2014 [87] LC-MS n = 10 TNL, n = 35 TL AF PGF2α, PGFM, PGE2, bicyclo-PGEM, PGA2, PGJ2 TL>TNL
n = 18 PTNL
19-OH-PGE2 no difference between TL and TNL
TNL>PTNL
Park et al 2016 [88] ELISA n = 132 PTL (n = 41 PTD) AF PGF2α high levels associated with low GA at delivery and PTD
Rosen et al 2019 [89] GC-NICI-MS n = 740 (n = 41 sPTD) urine PGF2α no difference in 3rd TM levels between term and preterm delivery
Eick et al 2020 [90] GC-NICI-MS n = 469 (n = 50 PTD) urine PGF2α levels at 20-24wks and 24-28wks higher in preterm than term group
associated with increased odds of PTB
Peiris et al 2020 [91] LC-MS n = 10 TNL, n = 28 TL AF PGF2α, PGFM, PGE2 TL>TNL
Takahashi et al 2021 [92] LC-MS n = 11 TNL, n = 10 TL AF PGE2, 15-keto-PGE2, PGEM, 19-OH-PGE2 TL>TNL
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Abbreviations: TLC = thin layer chromatography, NL = no labour, TL = term labour, LOD = limit of detection, RIA = radioimmunoassay, L = labour, TM = trimester, PTNL = preterm no labour, GC = gas chromatography, AF = amniotic fluid, TNL = term no labour, GC-MS = gas chromatography-mass spectrometry, PGFM = 13,14-dihydro-15-keto-PGF2α, t-PGFM = 5α,7α-dihydroxy 11-keto tetranor-prostane 1,16-dioic acid, GA = gestational age, PC = paper chromatography, TD = transmission densitometry, CS = Caesarean section, PTL = preterm labour, PTD = preterm delivery, bicyclo-PGEM = 11-deoxy-13,14-dihydro-15-keto-11,16-bicyclo PGE2, PGEM = 13,14-dihydro-15-keto-PGE2, PTL-TD = preterm labour-term delivery, PTL-PTD = preterm labour-preterm delivery, PT = preterm, ELISA = enzyme-linked immunosorbent assay, PPROM = preterm premature rupture of membranes, LC-MS = liquid chromatography-mass spectrometry, NICI = negative ion chemical ionization, sPTD = spontaneous preterm delivery.

Fig 2. Prostaglandin metabolism pathway.

Fig 2

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n denotes the number of studies that measured the prostaglandin/metabolite. Abbreviations: AF = amniotic fluid, PL = plasma, UR = urine, SE = serum, COX 1/2 = cyclooxygenase 1/2, PGFM = 13,14-dihydro-15-keto-PGF2α, PGEM = 13,14-dihydro-15-keto-PGE2, bicyclo-PGEM = 11-deoxy-13,14-dihydro-15-keto-11,16-bicyclo PGE2, t-PGFM = 5α,7α-dihydroxy-11-keto tetranor-prostane-1,16-dioic acid.

Table 3. Range of prostaglandin concentrations reported using different measurement techniques.

Biofluid PG/Metabolite Measurement Technique Range
plasma PGE RIA NL: 4.8 [36]–3641.2 [69] (pg/ml), L: 5.4 [36]–2429.1 [69] (pg/ml)
PGF RIA NL: 6.2 [36]–480 [29] (pg/ml), L: 7.9 [36]–600 [29] (pg/ml)
PGE2 TLC and biological assay NL: <200 pg/ml [10]
RIA NL: 4.6 [60]–15,600 [55] (pg/ml), L: 559 [50]–21,200 [55] (pg/ml)
PGE1 TLC and biological assay <200 pg/ml [10]
RIA NL: 2600 [55]–10,000 [33] (pg/ml), L: 4500 [64]–6800 [33] (pg/ml)
PGF2α TLC and biological assay NL: <200 pg/ml [10], L: <200 [10]– 18,000 [10] (pg/ml)
GC-MS NL: <70 [19]–600 [19] (pg/ml), L: <100 [19]–200 [19] (pg/ml)
RIA NL: 17 [37]–4600 [55] (pg/ml), L: 33.1 [21]–7900 [55] (pg/ml)
PGF1α TLC and biological assay NL: <200 pg/ml [10]
6-keto-PGF1α GC-MS NL: 131 [42]–244 [42] (pg/ml)
RIA NL: 18.7 [53]–318.6 [58] (pg/ml), L: 21 [70]–608 [70] (pg/ml)
PGEM RIA NL: 58 [54]–554 [77] (pg/ml), L: 82 [54]–433 [77] (pg/ml)
Bicyclo-PGEM RIA NL: 49 [48]–200 [59] (pg/ml), L: 62 [48]–500 [75] (pg/ml)
PGFM GC-MS NL: 31 pg/ml [19], L: 267–942 [19] (pg/ml)
RIA NL: 32.1 [61]–490 [41] (pg/ml), L: 20 [75]–2880 [58] (pg/ml)
serum PGF2α RIA NL: 200 [13]–1800 [12] (pg/ml), L: 100 [11]–3000 [11] (pg/ml)
AF PGE RIA NL: 89 [37]–1400 [29] (pg/ml), L: 502.8 [69]–8800 [29] (pg/ml)
PGF RIA NL: 50 [27]–1650 [24] (pg/ml), L: 500 [27]–75,000 [27] (pg/ml)
PGE2 GC NL: <200 [16]–6200 [16] (pg/ml), L: 1200 [16]–17,000 [16] (pg/ml)
PC and TD NL: 250 [26]–300 [26] (pg/ml), L: 1700 pg/ml [26]
RIA NL: <10 [30]–11,177 [80] (pg/ml), L: 17.8 [76]–28,197 [74] (pg/ml)
ELISA NL: 24 [85]–4749 [85] (pg/ml), L: 62 [85]–36,651 [85] (pg/ml)
LC-MS NL: <10 [87]–70,493 [87] (pg/ml), L: <10 [87]–105,739 [87] (pg/ml)
PGE1 GC NL: <500 pg/ml [16], L: <500 pg/ml [16]
PC and TD NL: 1000 [26]–1200 [26] (pg/ml), L: 1800 pg/ml [26]
RIA NL: <10 [30]–5000 [33] (pg/ml), L: 4400 pg/ml [33]
PGF2α RIA NL: 29 [66]–4700 [26] (pg/ml), L: 27 [71]–44,270 [33] (pg/ml)
ELISA NL: 8 [85]–926 [85] (pg/ml), L: 78 [85]–15,326 [85] (pg/ml)
LC-MS NL: <10 [87]–127 [91] (pg/ml), L: <10 [87]–42,537 [87] (pg/ml)
PGF1α PC and TD NL: 1500 [26]–2000 [26] (pg/ml), L: 12,000 pg/ml [26]
6-keto-PGF1α RIA NL: 67 [79]–809 [80] (pg/ml), L: 68 [80]–1927 [80] (pg/ml)
PGEM LC-MS NL: 71 pg/ml [92], L: 8425 pg/ml [92]
Bicyclo-PGEM RIA L: 75 [71]- 4275 [71] (pg/ml)
LC-MS NL: <10 [87]–66,900 [87] (pg/ml), L: 8361 [87]–133,800 [87] (pg/ml)
15-keto-PGE2 LC-MS NL: 0 pg/ml [92], L: 210.24 pg/ml [92]
19-OH-PGE2 LC-MS NL: 0 [92]–221,100 [87] (pg/ml), L: 73.7 [92]–202,675 [87] (pg/ml)
PGFM RIA NL: 80 [79]–1571 [79] (pg/ml), L: 105 [71]–25,028 [56] (pg/ml)
LC-MS NL: <10 [87]–114.79 [91] (pg/ml), L: <10 [87]–28,360 [87] (pg/ml)
PGD2 RIA L: 900 [63]–1800 [63] (pg/ml)
PGJ2 LC-MS L: 8542.8 pg/ml [87]
9α,11β-PGF2 ELISA NL: 30 [84]–204 [84] (pg/ml), L: 10 [84]–396 [84] (pg/ml)
PGA2 LC-MS NL: <10 [87]–16,722 [87] (pg/ml), L: <10 [87]–50,167 [87] (pg/ml)
urine PGF2α RIA NL: 0.99 [83]–1.85 [83] (pg/g creatinine), L: 2.03 [83]–3.14 [83] (pg/g creatinine)
GC-NICI-MS NL: 1840 [90]–2060 [89] (pg/ml)
6-keto-PGF1α RIA NL: 114,000 [67]–571,000 [67] (pg/g creatinine), L: 426,980 [62]–1,219,000 [67] (pg/g creatinine)
PGFM RIA NL: 1.82 [83]–4.87 [83] (pg/g creatinine), L: 7.93 [83]–12.70 [83] (pg/g creatinine)
t-PGFM RIA NL: 0.46 [20]–2.32 [20] (μg/hr), L: 1.06 [41]–2.50 [31] (μg/hr)
2,3-dinor-6-keto-PGF1α ELISA NL: 623,232 [81]–1,096,181 [81] (pg/ml)
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Published data presented as ng/ml, nanomolars, or picomolars were converted to pg/ml and data presented as ng/g creatinine or ng/mmol creatinine were converted to pg/g creatinine. Data published in μg/hr were not converted and are presented as in the original article.

Abbreviations: RIA = radioimmunoassay, NL = non-labour, L = labour, TLC = thin layer chromatography, GC-MS = gas chromatography-mass spectrometry, PGEM = 13,14-dihydro-15-keto-PGE2, bicyclo-PGEM = 11-deoxy-13,14-dihydro-15-keto-11,16-bicyclo PGE2, PGFM = 13,14-dihydro-15-keto-PGF2α, PC = paper chromatography, TD = transmission densitometry, ELISA = enzyme-linked immunosorbent assay, LC-MS = liquid chromatography-mass spectrometry, GC-NICI-MS = gas chromatography-negative ion chemical ionization-mass spectrometry, t-PGFM = 5α,7α-dihydroxy 11-keto tetranor-prostane 1,16-dioic acid.

Amniotic fluid

Labour and non-labour

In total, 25 studies compared amniotic fluid prostaglandins in labour vs non-labour. PGF2α increased in labouring participants compared to non-labouring participants in most studies, (Table 2) however, one study found no difference [69]. Similarly, PGE2 was reported to increase with labour in 11/12 studies [16, 26, 30, 66, 78–80, 82, 87, 91, 92]. 6-keto-PGF1α and PGFM and were reported to increase in labouring participants compared to non-labouring participants in three [40, 79, 80] and seven [32, 41, 78–80, 87, 91] studies, respectively. Results were mixed for PGE1 [26, 33]. PGE was not found to increase with labour [29, 69].

Prior to labour onset

Of the included amniotic fluid studies, 15 measured prostaglandins at more than one time point throughout pregnancy. PGF2α, PGF1α, and PGF were generally found to increase around term or prior to labour [17, 22, 27, 28, 30, 37, 52, 82, 85], though two studies found no pattern throughout pregnancy [26, 33]. Among studies that measured PGE or PGE2, most (4/6) reported increased levels around 35–36 weeks [30, 37, 52, 85]. PGE1 was not found to change with gestational age [26, 30, 33].

Predicting preterm labour

Six amniotic fluid studies investigated prostaglandins as predictors of preterm labour. Some studies suggest that PGF2α may be predictive of preterm delivery in those with threatened preterm labour [88] and PPROM [86] however, results are mixed [71, 73]. PGFM and bicyclo-PGEM were found in higher levels in participants with preterm labour leading to preterm delivery compared to those who eventually delivered at term [71]. Increased PGE2 levels may be predictive of delivery before term [68, 73] and before 34 weeks [76].

Blood

Labour and non-labour

In total, 27 studies compared labour and non-labour groups. PGF2α was reported to increase with labour compared to non-labour in most (6/8) studies [21, 41, 50, 55, 69, 72]. All 15 studies that measured PGFM reported higher levels in labour compared to non-labour (Table 2). Three studies measuring PGE reported varying results [29, 36, 69]. PGE1, PGE2, and PGF were all generally found to remain unchanged with labour [29, 33, 36, 50, 55].

Prior to labour onset

In total, 18 studies obtained measurements of plasma throughout pregnancy. Among those that measured PGF2α, some found increasing levels at or near term [37, 50, 60] however results were conflicting [21, 28, 33]. In 5/6 studies PGFM was not found to change with increasing gestational age [39, 41, 43, 44, 58]. Results for PGF2α in serum were mixed [11–13].

Predicting preterm labour

Two studies investigated prostaglandins in plasma as predictors of preterm labour. One study found that PGFM levels were higher in participants in preterm labour who delivered preterm compared to those who went on to deliver at term [61] though the other study reported no significant difference [44].

Urine

Labour and non-labour

Five studies compared labouring vs non-labouring groups. The metabolite t-PGFM was reported to increase with labour compared to non-labour [20, 31, 41].

Prior to labour onset

Four studies measured changes in prostaglandins in urine throughout pregnancy. PGF2α, PGFM, and t-PGFM were reported to increase around term, though this was only reported in one study for each prostaglandin/metabolite [20, 83]. The metabolite 2,3-dinor-6-keto-PGF1α did not change throughout pregnancy [81].

Predicting preterm labour

Two urine studies investigated prostaglandins as predictors of preterm labour. One found that PGF2α levels in urine samples collected at median 32.1 weeks were not significantly different between participants who delivered at term and those that delivered preterm [89]. In contrast, averaged levels of PGF2α in urine were also found to be associated with increased odds of preterm birth (OR = 1.98) [90].

Serial prostaglandin measurement during spontaneous labour

Although not a primary study question of this review, we noted that n = 40 studies obtained serial samples during labour. In general, prostaglandins measured in amniotic fluid increased throughout labour. Results were mixed for those that measured plasma and serum.

Quality assessment

Scores from the quality assessment were distributed as follows: 17% scored between 0–3, 41% scored between 4–6, and 42% scored between 7–9. The areas with the lowest scores were researcher blinding and sufficiency of sample number for internal validity. Scores for each study can be found in S3 Table.

Discussion

We demonstrate, through a systematic review of the literature investigating prostaglandins and metabolites in peripheral biofluids during pregnancy and labour, that prostaglandins of the PGE and PGF families do exhibit changes through pregnancy and labour, though results are inconsistent and inconclusive. Changes in PGE2, PGF2α, and PGFM levels with labour are most prominent in amniotic fluid, and to a lesser extent in blood. Similarly, our synthesis suggests that PGE2, PGF2α and PGF1α increase in amniotic fluid as pregnancy progresses and peak around term, though in plasma, a consistent pattern is unclear. Patterns in urine prostaglandin levels were inconclusive due to a relatively small number of studies investigating this biofluid. An important limitation is a general lack of data on prostaglandins and metabolites outside the PGE and PGF families, and as such we are unable to comment on their potential role in pregnancy and labour. Further, few studies examined prostaglandins as biomarkers for preterm labour and more research is needed to provide conclusive evidence for which prostaglandins or metabolites examined could offer the best options for prediction.

Measurement techniques for prostaglandins

Inconsistent study designs and methods greatly limited our ability to compare findings across studies. Up to the late 1990’s, researchers most commonly used radioimmunoassay techniques, which can be highly sensitive, but are often limited by the specificity of the antibody used and the potential of antibody cross-reactivity with similar molecules [87]. One study included in this review developed and reported on a radioimmunoassay for PGF2α with a cross-reactivity with PGF1α of 12.2% [17], which may have obscured patterns in PGF2α and made it difficult to ascertain fine-tuning of the prostaglandin pathway among similar molecules. Furthermore, multiple other studies using radioimmunoassay techniques were unable to differentiate between PGF2α and PGF1α, and PGE2 and PGE1 and therefore could only report on levels of PGF and PGE, respectively, making it difficult to compare the results of these studies with others. Lack of specificity and accuracy in these radioimmunoassay techniques may have contributed to the discrepancies across results and highlights the importance of re-visiting dogma in light of novel evidence and technologies. In contrast, the high specificity and sensitivity of mass spectrometry for lipid identification suggests that this method may be more suitable and accurate for measurement of prostaglandins [87]. Additionally, the capability of mass spectrometry to co-assess multiple prostaglandins and metabolites can provide a quantitative profile of prostaglandins before and during labour, as well as identify prostaglandins and/or metabolites not previously measured that may play a role in pregnancy and/or labour [93].

Considerations among unique biofluids

Among the studies included in this review, the most assayed biofluid was maternal plasma. Although an appealing fluid due to its ability to be sampled relatively easily, results from measurements in plasma were often conflicting, especially among studies that measured primary prostaglandins. Accurate measurement of changes in primary prostaglandin levels in blood is complicated by their rapid metabolism and correspondingly short half-life [94, 95]. This difficulty is further compounded by the production of prostaglandins by platelets that occurs during isolation of plasma and storage of samples [36, 96, 97]. Measurement of plasma PGFM appears to be a good alternative for PGF2α, as there is no evidence that this metabolite is formed during sample collection or isolation and therefore may more accurately reflect endogenous prostaglandin production [19]. The primary metabolite of PGE2, however, is chemically unstable [98], which necessitates the measurement of its degradation product, bicyclo-PGEM, for an accurate index of PGE2 production [99]. Therefore, results from early studies measuring primary prostaglandins and/or PGEM in plasma and/or serum should be interpreted with these considerations in mind and future studies in blood should aim to measure PGFM or bicyclo-PGEM as indices of PGF2α or PGE2 production, respectively.

Amniotic fluid lacks prostaglandin metabolizing enzymes [100, 101], which suggests that measurement of the primary prostaglandins in this fluid may be more accurate than in serum or plasma. However, sampling amniotic fluid is more difficult and may introduce infections harmful to the developing fetus, making this fluid impractical as a predictive resource. Additionally, prostaglandin levels vary based on method of collection and region of the amniotic sac [102, 103] which complicates any interpretation of results from studies and limits the clinical utility of an amniotic fluid test for prediction of preterm labour.

Measurement of the main urinary metabolite of PGF2α may be preferable to measuring PGFM in plasma or serum in some cases, as a significant portion of circulating PGF2α is eventually excreted into the urine [104]. In the present investigation, we identified only nine studies that assayed urine, and we suggest that the presence of urinary metabolites of prostaglandins during pregnancy and labour merits further study.

Demographic and clinical information

Among the articles included in this review, we noted that very few provided complete demographic and clinical information on their participants. Factors including age, race/ethnicity, membrane status, and gravidity/parity may impact prostaglandins levels and a lack of consideration for these variables may obscure patterns of prostaglandin levels throughout pregnancy and labour. Complete descriptions of gestational age groups and clearly defined outcomes for both term and preterm labour would additionally make studies more easily comparable. As well, preterm labour is generally defined as labour occurring before 37 weeks gestation, however the pathophysiological processes involved in extreme preterm birth (<28 weeks) may vary dramatically from those near term [105]. Therefore, stratification of outcome groups based on gestational age at delivery may be more informative, though would require larger sample sizes to maintain statistical power.

Role for other prostaglandins

While prostaglandins of the E and F series are most clinically targeted for labour management, there is evidence to suggest that other members of the prostaglandin family may play a role in pregnancy and labour. For example, PGD2 has been shown to increase uterine contractility and blood flow in various mammals [106–108] and is associated with cervical dilation in humans [63]. Two metabolites of PGD2, 9α,11β-PGF2 and PGJ2, were each identified only once among the articles included in this review and were both reported to increase with term labour [84, 87]. These metabolites may be of interest to future researchers, as the development of new methodologies such as mass spectrometry have allowed for more accurate and sensitive measurements of select members of the prostaglandin pathway.

Limitations

The main limitation of this review is that only studies in English or with an available English translation were included, which may have resulted in missing some relevant articles. However, current resources limited the feasibility of including non-English studies.

Conclusion

We have identified evidence to suggest that prostaglandin levels, particularly within the PGE and PGF families, do increase in some biofluids during pregnancy and labour. However, changing prostaglandin levels throughout pregnancy and labour are likely highly complex and warrant further investigation, including serial measurements with more precise methodologies in higher-powered studies. Two important limitations identified in this review are the lack of data on the complexity of the prostaglandin pathway outside of the PGE and PGF families and the inherent difficulty in measuring primary prostaglandins in blood, due to their short half-lives in this biofluid. With the advent of i) new methodologies that can assess multiple prostaglandins and metabolites together, ii) a more developed understanding of the range of prostaglandins and iii) a better understanding of the heterogeneous nature of term and preterm labour, future studies that take each of these parameters into account in their study design will help provide further insight into the changing levels of prostaglandins in pregnancy and labour.

Supporting information

S1 Table. EMBASE search terms.

(DOCX)

Click here for additional data file. (86.8KB, docx)
S2 Table. MEDLINE search terms.

(DOCX)

Click here for additional data file. (92KB, docx)
S3 Table. Quality assessment scores.

(DOCX)

Click here for additional data file. (62.7KB, docx)
S1 Checklist. PRISMA 2020 checklist.

(DOC)

Click here for additional data file. (1.9MB, doc)

Acknowledgments

We would like to acknowledge SL Wood for his help with this systematic review.

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

This study was supported by the Canadian Institutes of Health Research (CIHR) to DMS (CIHR grant PJT-173295), a University of Calgary, Eye’s High Doctoral Recruitment Scholarship to KKH and an NSERC Undergraduate Student Research Award to EMW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Tamas Zakar

30 Sep 2021

PONE-D-21-27902Prostaglandins in biofluids in pregnancy and labour: a systematic reviewPLOS ONE

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Reviewer #1: ORIGINALITY: This is a systematic review of studies measuring endogenous prostaglandins in blood, amniotic fluid and urine during pregnancy and spontaneous labour. Previous systematic reviews have focussed on exogenous prostaglandins for the induction of labour (eg. Alfirevic et al BMJ 2015;350:h217 and BJOG 2016;123:1462-70). Other recent reviews of the mechanism of parturition have been non-systematic and more wide-ranging (eg Vannuccini et al, Annales d’Endocrinologie 2016;77:105-13, and Mendelson et al, J Steroid Biochem Mol Biol 2017;170:19-27). These show little or no overlap with the present study and I can find no similar systematic reviews. This paper is therefore original.

SCIENTIFIC RELIABILITY: The authors have used the standard methodology of systematic reviews. The inclusion and exclusion criteria are appropriate, as are the quality assessment criteria. I am not aware of any studies that have been missed, and the Discussion seems to me to be balanced and objective.

At line 286 the authors refer to the short half-life of prostaglandins in blood, which I think is the major factor limiting the clinical application of these studies. This issue is discussed clearly in this section of the paper (lines 281-308) but might be worth mentioning again in the Conclusion.

CLINICAL IMPORTANCE: The purpose of this review is to provide a basis for future studies aimed at improving the clinical monitoring of pregnancies. The paper in itself does not have direct clinical relevance but I believe it is very helpful in giving an authoritative overview of what we know (and more importantly, don’t know) about endogenous prostaglandins in pregnancy. It fills an important gap in the literature and I believe it will be widely cited.

OTHER COMMENTS: The paper satisfies the criteria for publication in PLOS ONE. It is original and as far as I know the results have not been published elsewhere. The review is described in sufficient detail and the conclusions are appropriate. The English is excellent and the data availability standards have been met.

Reviewer #2: In this study, the authors systematically searched and summarized the existing scientific literature related to detection of prostaglandins levels in biofluids of pregnancy women, including amniotic fluid, plasma and urine, which in order to determine how biofluid levels of prostaglandins change throughout pregnancy before and during labor. This is a well-written review with comprehensive summary. Although the methods of detecting prostaglandins were diverse in different studies, this manuscript will be improved if the authors can summarize the concentrations of prostaglandins measured under the same methods.

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Reviewer #1: No

Reviewer #2: No

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PLoS One. 2021 Nov 18;16(11):e0260115. doi: 10.1371/journal.pone.0260115.r002

Author response to Decision Letter 0

26 Oct 2021

Journal requirements:

Comment 1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: To ensure that our manuscript meets PLOS ONE’s style requirements the beginning line of each paragraph has been indented, author names on the title page have been reformatted (line 7, page 1), and postal codes and street addresses have been removed from the author affiliations on the title page (lines 9-13, page 1). In addition, the file S1_File has been renamed “PRISMA checklist” in accordance with journal requirement.

Comment 2: We note that you have included the phrase “date not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

Response: We have removed the phrase “not shown” from the manuscript, as it referred to data that is not shown in Figure 2, but which is included in Table 2 and within the manuscript text.

Comment 3: We note that this manuscript is a systematic review or meta-analysis; our author guidelines therefore require that you use PRISMA guidance to help improve reporting quality of this type of study. Please upload copies of the completed PRISMA checklist as Supporting Information with a file name “PRISMA checklist”

Response: Our completed PRISMA checklist was previously named S1_File and we have renamed it as “PRISMA checklist” and uploaded it as supporting information.

Comment 4: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response: We have reviewed our reference list and confirmed that we have not referenced any retracted articles. To ensure that the reference list is formatted correctly based on the Vancouver reference style guidelines, we have changed all journal names to their abbreviated versions. Additionally, the reference list was re-ordered slightly to ensure that all references are numbered in the order that they appear in the text (i.e., the reference for Ananth et al. (2013) was moved to position 105 in the reference list (originally position 108) and all the following references moved up one position in the reference list).

Reviewer’s Comments to the Authors

Reviewer 1:

Comment 1: At line 286 the authors refer to the short half-life of prostaglandins in blood, which I think is the major factor limiting the clinical application of these studies. This issue is discussed clearly in this section of the paper (lines 281-308) but might be worth mentioning again in the Conclusion.

Response: We agree with this comment – thank you. Therefore, we have added lines 363-366 (page 29) to the conclusion to reiterate the major limitations identified in this review, including the inability to measure primary prostaglandins in blood due to their short half-lives.

Reviewer 2:

Comment 1: Although the methods of detecting prostaglandins were diverse in different studies, this manuscript will be improved if the authors can summarize the concentrations of prostaglandins measured under the same methods.

Response: We agree with this suggestion – thank you. Therefore, we have added Table 3 on pages 18-20, which includes the range of prostaglandin concentrations reported under each measurement technique. Lines 156-157 on page 9 refer to this table.

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (122.6KB, docx)

Decision Letter 1

Tamas Zakar

3 Nov 2021

Prostaglandins in biofluids in pregnancy and labour: a systematic review

PONE-D-21-27902R1

Dear Dr. Slater,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Tamas Zakar

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The authors have adequately addressed all comments.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Acceptance letter

Tamas Zakar

9 Nov 2021

PONE-D-21-27902R1

Prostaglandins in biofluids in pregnancy and labour: a systematic review

Dear Dr. Slater:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr Tamas Zakar

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. EMBASE search terms.

    (DOCX)

    Click here for additional data file. (86.8KB, docx)
    S2 Table. MEDLINE search terms.

    (DOCX)

    Click here for additional data file. (92KB, docx)
    S3 Table. Quality assessment scores.

    (DOCX)

    Click here for additional data file. (62.7KB, docx)
    S1 Checklist. PRISMA 2020 checklist.

    (DOC)

    Click here for additional data file. (1.9MB, doc)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (122.6KB, docx)

    Data Availability Statement

    All relevant data are within the paper and its Supporting information files.

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