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Background:
Systematic Review

Association Between Maternal C-Reactive Protein (CRP) Levels and Adverse Neonatal Outcomes: A Systematic Review and Meta-Analysis

by
Rutaba Mahereen
1,
Abdullah Alsatli
1,
Faiza Said Albader
1,
Rawan Ibrahim Alqabbaa
2,
Lamar Abu Shehadeh
1,
Mohamad Behairy
1,
Ghezlan Alaliw
1,
Lamees Tarek Alzahrani
1,
Maria Abdulaziz Alrafi
2,
Nojoud Sulaiman Alganas
2,
Noor A Altaho
3,
Saeed Baradwan
4,
Ayman MA Mohamed
1 and
Ahmed Abu-Zaid
1,*
1
College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia
2
College of Medicine, Almaarefa University, Riyadh 13713, Saudi Arabia
3
Department of Pediatrics, Al-Amiri Hospital, Kuwait City 13001, Kuwait
4
Department of Obstetrics and Gynecology, King Faisal Specialist Hospital and Research Center, Jeddah 23433, Saudi Arabia
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2026, 15(6), 2114; https://doi.org/10.3390/jcm15062114
Submission received: 23 January 2026 / Revised: 24 February 2026 / Accepted: 8 March 2026 / Published: 10 March 2026
(This article belongs to the Special Issue New Perspectives in Maternal–Fetal Medicine)
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Versions Notes

Abstract

Background: C-reactive protein (CRP), a biomarker of systemic inflammation, has been implicated in adverse pregnancy and neonatal outcomes. However, the relationship between maternal CRP and neonatal complications remains unclear. We conducted a systematic review and meta-analysis to synthesize available evidence. Methods: We systematically searched PubMed, Scopus, Web of Science, and Cochrane Library up to July 2025 for observational studies reporting maternal CRP levels in relation to neonatal outcomes. Eligible outcomes included preterm birth (PTB), low birth weight (LBW), small for gestational age (SGA), and stillbirth. Random-effects models were used to calculate pooled standardized mean differences (SMD) or odds ratios (OR) with 95% confidence intervals (CI). Statistical heterogeneity was assessed using the I2 statistic. Results: The search yielded 6843 records, of which 42 studies (comprising 18,393 pregnant women) met the inclusion criteria. Maternal mean CRP levels were significantly higher in adverse pregnancy outcomes compared with controls (SMD = 0.39; 95% CI 0.08–0.70; p = 0.01; I2 = 96.6%). Elevated CRP was strongly associated with PTB (OR = 3.81; 95% CI 2.66–5.47; p < 0.001; I2 = 85%; n = 23) and LBW (OR = 2.34; 95% CI 1.35–4.03; p = 0.002; I2 = 84.2%; n = 7). No significant associations were observed for SGA (OR = 1.14; 95% CI 0.86–1.49; p = 0.36; I2 = 0%; n = 5) or stillbirth (OR = 1.89; 95% CI 0.92–3.90; p = 0.08; I2 = 44.9%; n = 4). Conclusion: Maternal CRP is significantly associated with increased risks of preterm birth and low birth weight but not with SGA or stillbirth. These findings support the role of systemic inflammation in adverse neonatal outcomes and highlight the need for prospective studies to clarify causal mechanisms and assess the clinical utility of CRP in pregnancy risk stratification.

1. Introduction

Pregnancy is a highly complex physiological process that requires the balanced adaptation of maternal, placental, and fetal systems to ensure healthy development [1,2]. Despite significant advances in obstetric and neonatal care, neonatal complications remain a major contributor to global morbidity and mortality [3,4]. Each year, an estimated 13.4 million infants are born preterm (before 37 weeks of gestation), accounting for nearly 1 in 10 live births worldwide [5]. Furthermore, 20.5 million babies are born with a low birth weight (LBW, <2500 g) [6,7]. Preterm birth (PTB) complications are the leading cause of death in children under 5 years of age, responsible for approximately 1 million neonatal deaths annually [8,9]. LBW significantly increases a newborn’s vulnerability, elevating the risk of infections, impaired neurodevelopment, and chronic diseases such as diabetes and hypertension later in life [10,11,12]. These challenges are compounded by other conditions; being small for gestational age (SGA) affects an estimated 27% of live births (nearly one in four newborns globally) [13,14].
It has been demonstrated that systemic and chronic inflammation is increasingly recognized as a key pathway in adverse pregnancy and neonatal complications [15,16,17]. During a normal pregnancy period, the maternal immune system balances tolerance to the fetus by increasing its protection against pathogens and inflammation [18]. When this balance is disrupted, increased inflammatory responses can compromise placental function and fetal growth [19]. C-reactive protein (CRP), an acute-phase protein synthesized by the liver in response to interleukin-6 and other cytokines, is a widely used marker of systemic inflammation [20,21]. In pregnancy, higher CRP levels have been observed in association with infections, gestational disorders, and placental pathology, making it a biologically plausible marker for adverse neonatal outcomes [22,23,24].
Several observational studies have evaluated maternal CRP levels in relation to neonatal complications [25,26,27]. Elevated CRP has been associated with PTB and LBW in multiple reports, suggesting that systemic inflammation may trigger early labor and restrict fetal growth [28,29,30]. However, associations between high CRP levels with SGA and stillbirth are less consistent [31]. Some studies report elevated risks, while others find no significant relationship [32]. These discrepancies may be explained by differences in study design, population characteristics, timing of CRP measurement, or laboratory methods. As a result, the overall strength and reliability of the evidence remain uncertain [33].
Given the burden of neonatal complications and the potential role of maternal inflammation in their development, a systematic review and meta-analysis is warranted. No prior synthesis has comprehensively evaluated maternal CRP levels across PTB, LBW, SGA, and stillbirth. Clarifying these associations could improve our understanding of inflammatory mechanisms in pregnancy, help refine risk stratification, and inform preventive strategies. This systematic review and meta-analysis therefore aim to critically appraise and pool the available evidence on maternal CRP as a biomarker of adverse neonatal outcomes.

2. Methods

2.1. Study Protocol

This meta-analysis adhered to rigorous methodological standards, incorporating both the MOOSE guidelines for observational studies and the PRISMA framework for systematic reviews [34,35,36]. The PRISMA checklist is found in Supplementary File S1. To minimize bias, two independent reviewers carried out the literature search, study selection, data extraction, and quality appraisal. Any discrepancies between reviewers were addressed through consultation with a third investigator. Additionally, the protocol for this review was prospectively registered with PROSPERO (CRD420251149463).

2.2. Search Strategy

This systematic review and meta-analysis study was conducted by reviewing articles from inception to July 2025. Articles were extracted using related keywords in most related to the field databases, including PubMed, Scopus, Web of Science, and Cochrane Library. We implemented the search strategy in English, according to the search engine, including the following keywords: “CRP” OR “hs-CRP” OR “C-reactive protein” OR “high-sensitivity C-reactive protein” OR “ultrasensitive CRP” OR “serum CRP” OR “plasma CRP” AND “Pregnancy Outcome” OR “Premature Birth” OR “Stillbirth” OR “Intrauterine Fetal Death” OR “IUFD” OR “Infant, Low Birth Weight” OR “Low Birth Weight” OR “LBW” OR “Fetal Growth Retardation” OR “IUGR” OR “Fetal Macrosomia” OR “Infant, Small for Gestational Age” OR “SGA” OR “Asphyxia Neonatorum” OR “Birth Asphyxia” (details on Supplementary File S2). This search was complemented by perusal of the references of related review studies and retrieved articles.

2.3. Eligibility Criteria and Study Selection

The included studies consisted of randomized and non-randomized prospective or retrospective cohorts and case–control investigations that evaluated the relationship between maternal CRP levels and neonatal outcomes. To be eligible, studies had to specifically investigate this correlation, while exclusion criteria encompassed unrelated or duplicate publications, studies where neonatal outcomes were not examined or were incorrectly defined, and those for which the full text was unavailable, in addition to review articles, case reports, case series, and editorials. Variables of interest in this study included PTB (delivery before 37 completed weeks of gestation) [37], LBW (birth weight less than 2500 g) [38], SGA (birth weight below the 10th percentile for gestational age) [39], and stillbirth (fetal death at or after 20 weeks of gestation) [40].

2.4. Data Extraction, Quality of Studies, and Certainty of Evidence

Two reviewers independently performed data extraction and quality assessment in duplicate, resolving disagreements through discussion and contacting study authors when clarification was needed. Extracted data included: (i) study characteristics such as setting, country, population demographics, and trimester of CRP measurement; (ii) outcome definitions and timing for PTB, LBW, SGA, and stillbirth; (iii) publication and exposure details including year, type of CRP measured (e.g., hs-CRP), and cut-off values for elevated CRP; (iv) analytical variables necessary for synthesis; and (v) special considerations such as prioritizing intended over actual mode of delivery when both were reported. Risk of bias was assessed separately using the Newcastle–Ottawa Scale (NOS) [41], which evaluates observational studies across three domains: (i) selection of study groups, (ii) comparability of groups, and (iii) ascertainment of exposure or outcome, with higher scores indicating lower risk of bias. In addition, the overall certainty of evidence for each outcome was graded using the GRADE approach [42], considering risk of bias, inconsistency, indirectness, imprecision, and publication bias.

2.5. Statistical Analysis

Meta-analyses were conducted using the DerSimonian and Laird random-effects model [43] in Stata 18.0 (Stata Statistical Software: Release 18. College Station, TX, USA: StataCorp LLC; 2023). A random-effects approach was selected due to expected heterogeneity in study designs and populations. For continuous variables such as maternal CRP levels, effect sizes were expressed as standardized mean differences (SMDs), while dichotomous outcomes were reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). When studies reported outcomes across different time periods or distinct variables, these were pooled separately. Subgroup analyses were performed based on maternal age, CRP type, CRP cut-offs, and the trimester during which CRP was measured. Sensitivity analyses were conducted to examine the influence of individual studies on pooled results where possible. Statistical heterogeneity was assessed using the χ2 test for homogeneity and quantified with the I2 statistic, with I2 ≥ 50% considered indicative of substantial heterogeneity [44]. Publication bias was evaluated by visual inspection of funnel plots, supplemented by Begg’s rank correlation test and Egger’s regression test.

3. Result

3.1. Study Selection

Our initial database search yielded 6843 records (PubMed = 1794, Web of Science = 431, Scopus = 4459, Cochrane = 159). After removing 1441 duplicates, a total of 5402 records were screened. Of these, 5331 records were excluded following title and abstract review. We assessed 71 full-text articles for eligibility, of which 29 were excluded (21 due to irrelevant outcomes, 7 for insufficient data reporting, and 1 for lack of an appropriate control group) [31,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72]. Eventually, 42 studies [25,26,27,28,32,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109] met the inclusion criteria and were included in the systematic review and meta-analysis. The study selection process of this review is presented in Figure 1.

3.2. Main Characteristics of the Included Studies

A total of 18,393 pregnant women were included across 42 studies conducted in Asia (India, China, South Korea, Indonesia, Iran, Iraq), Europe (UK, Spain, Netherlands, Denmark, Poland, Bulgaria, Ireland, Turkey), North America (USA), South America (Brazil), and Africa (Egypt). Sample sizes ranged from 48 participants in Indonesia to 6016 participants in the Netherlands. Most studies were prospective cohorts (n = 26), with additional designs including case–control (n = 12), retrospective cohort (n = 2), cross-sectional (n = 2), and nested case–control/nested cohort (n = 2). CRP was measured at different gestational stages: 1st trimester (n = 10 studies), 2nd trimester (n = 11), 3rd trimester (n = 13), and mixed trimesters (n = 8). Regarding biomarker type, 28 studies assessed CRP and 14 measured hs-CRP. Participant ages ranged from the youngest reported (18–20 years in India) to the oldest (median of 37.9 years in the UK), with most studies including women in their mid-to-late twenties or early thirties. All study characteristics are summarized in Table 1.

3.3. Risk of Bias (NOS Assessment)

The methodological quality of the 42 included studies, assessed using the NOS, ranged from 4/9 to 9/9 stars. A total of 14 studies achieved the maximum score of 9/9, indicating low risk of bias. 10 studies scored 8/9, 6 studies scored 7/9, 6 studies scored 6/9, 5 studies scored 5/9, and 1 study scored 4/9. In the selection domain, 42/42 studies received ≥2 stars, reflecting adequate case and control selection. In the comparability domain, only 15 studies earned the maximum of 2 stars, while the remaining 27 studies received 0–1 star, indicating limited adjustment for confounders. In the outcome domain, 38 studies received 3 stars, while 4 studies were rated with 2 stars. These findings show that although a substantial proportion of studies were of high methodological quality, inadequate control for potential confounders was the most frequent limitation (Table 1).

3.4. Quantitative Synthesis

The pooled analysis demonstrated that maternal CRP levels were significantly higher in pregnancies with adverse neonatal outcomes compared with controls (SMD, 0.39; 95% CI, 0.08–0.70; p = 0.01; I2 = 96.6%, n = 28) (Figure 2A). Subgroup analyses showed that this association was strongest in women aged 20–30 years (SMD = 0.74; 95% CI: 0.28–1.21) and in studies measuring hs-CRP (SMD = 0.87; 95% CI: 0.15–1.58), as well as when CRP was assessed in the third trimester (SMD = 0.69; 95% CI: 0.28–1.10) (Table 2). Elevated maternal CRP was strongly associated with increased odds of PTB (OR = 3.81; 95% CI: 2.66–5.47; p < 0.001; I2 = 85%; n = 23) (Figure 2B). Subgroup analyses revealed stronger associations in studies using hs-CRP (OR = 6.84; 95% CI: 2.69–17.44), in those with CRP cut-offs of 3.0–5.9 mg/L (OR = 6.51; 95% CI: 3.20–13.25), and when CRP was measured in the third trimester (OR = 6.14; 95% CI: 2.88–13.09) (Table 2). A significant association was also observed between elevated CRP and LBW (OR = 2.34; 95% CI: 1.35–4.03; p = 0.002; I2 = 84.2%; n = 7) (Figure 2C). Subgroup analyses indicated that significant associations were evident for studies using conventional CRP (OR, 2.39; 95% CI: 1.08–5.30) and for those with CRP cut-offs of 6–8.9 mg/L (OR: 1.95; 95% CI, 1.11–3.44), with the effect being most pronounced when CRP was measured in the second trimester (OR = 2.89; 95% CI: 1.06–7.94) (Table 2). By contrast, no significant association was identified for SGA (OR = 1.14; 95% CI: 0.86–1.49; p = 0.36; I2 = 0%; n = 5) (Figure 2D). Subgroup analyses stratified by maternal age did not demonstrate significant results, with both younger (OR = 1.68; 95% CI: 0.42–6.67) and older (OR = 1.17; 95% CI: 0.52–2.61) groups showing null associations (Table 2). Similarly, no significant association was found for stillbirth (OR = 1.89; 95% CI: 0.92–3.90; p = 0.08; I2 = 44.9%; n = 4) (Figure 2E).
Sensitivity analyses, including leave-one-out testing, did not materially alter the pooled estimates for any of the outcomes, and detailed results are presented in Supplementary File S3. Moreover, Evidence of publication bias was detected for overall CRP levels (Egger’s p = 0.022) and for PTB (Egger’s and Begg’s p < 0.001), with possible bias also suggested for LBW (Egger’s p < 0.001). No significant publication bias was observed for SGA (Egger’s p = 0.388; Begg’s p = 0.221) or stillbirth (Egger’s p = 0.024; Begg’s p = 0.734) (Supplementary File S4). The certainty of evidence, assessed using GRADE, was rated as moderate for overall neonatal complications, PTB, and LBW, primarily downgraded due to inconsistency (I2 > 60%). In contrast, the certainty was low for SGA and stillbirth, reflecting limitations from indirectness (few studies) and imprecision (wide CIs) (Supplementary File S5).

4. Discussion

However, there are systematic reviews that have evaluated the correlation between CRP levels and maternal complications [23,110,111]. To the best of our knowledge, this is the first systematic review and meta-analysis to comprehensively evaluate the correlation between maternal CRP levels and neonatal complications. Our systematic review and meta-analysis of 42 observational studies involving 18,393 pregnant women show that elevated maternal CRP is significantly associated with PTB and LBW, but not with SGA or stillbirth. These findings highlight the importance of systemic inflammation during pregnancy as a risk factor for adverse neonatal outcomes, suggesting that CRP may have a role both as a biomarker for risk stratification and as a target for future interventions in obstetric care.
Our findings are broadly in alignment with Nikbakht et al. [26] and Lohsoonthorn et al. [91], who both reported that higher maternal CRP levels were associated with increased risks of PTB and impaired fetal growth. Similarly, Zhang et al. observed significant associations between elevated maternal CRP and adverse offspring outcomes in the context of neuropsychiatric disorders, further supporting the role of maternal inflammation in developmental health [112]. Lucaroni et al. [113] further demonstrated, through an umbrella review of biomarkers for spontaneous PTB, that CRP measured in maternal plasma was associated with an odds ratio of 2.0 for PTB, while CRP measured in amniotic fluid was associated with an odds ratio of 8.0, findings that reinforce our pooled estimates of CRP as a marker of prematurity risk. Wei et al. [114] also showed in a systematic review of inflammatory cytokines that elevated CRP in amniotic fluid was strongly associated with spontaneous PTB (OR = 7.85; 95% CI: 3.88–15.87), whereas plasma CRP showed only a modest association (OR = 1.53; 95% CI: 1.22–1.90), highlighting the stronger predictive role of local inflammatory responses at the maternal–fetal interface compared to systemic inflammation. By contrast, van de Laar et al. [115] concluded that maternal CRP had only moderate predictive accuracy for histological chorioamnionitis and was not a reliable predictor of neonatal sepsis in women with PPROM, highlighting that its predictive value may vary depending on clinical context and outcomes assessed. In addition, some studies such as Brown et al. [116] and Chudal et al. [117] reported no significant associations between maternal CRP and adverse offspring outcomes, which may be explained by differences in study design, smaller sample sizes, timing of CRP measurement, and the specific outcomes investigated compared with our review.
Several biological pathways may explain the associations observed between elevated maternal CRP and adverse neonatal outcomes. CRP is an acute-phase reactant primarily induced by cytokines such as IL-6 and TNF-α, which amplify downstream inflammatory cascades [118,119]. In pregnancy, this systemic response can accelerate prostaglandin synthesis through COX-2 activation, promote cervical remodeling and uterine contractility, and ultimately preterm labor [120,121]. Park et al. demonstrated that maternal plasma IL-6 strongly correlated with serum CRP and amniotic fluid IL-6, and independently predicted intra-amniotic infection and imminent preterm delivery, underscoring the link between cytokine activation, systemic CRP, and the timing of labor [27]. Subclinical intrauterine infections may further account for elevated CRP in women without overt clinical symptoms [122]. Even low-grade infections can trigger maternal and fetal immune responses, compromise placental integrity, and promote premature rupture of membranes, all of which increase the risk of PTB [17,123]. Lohsoonthorn et al. observed that CRP elevations were particularly associated with very early and medically indicated PTB, suggesting that inflammation is especially relevant in the most severe forms of prematurity [91]. This interpretation is strengthened by Mendelian randomization findings from Chen et al., which showed that genetically elevated maternal CRP increased the risk of PTB and reduced birth weight, supporting a causal link rather than simple correlation [124].
In addition to its role in preterm delivery, inflammation associated with elevated CRP can impair placental perfusion and vascular remodeling, disrupt trophoblast invasion, and interfere with nutrient and oxygen transport [125,126]. These processes contribute to fetal growth restriction, manifesting clinically as LBW [127]. Ernst et al. reported that higher CRP levels early in pregnancy predicted growth restriction, supporting placental dysfunction as a central mechanism [32]. Evidence of abnormal uteroplacental blood flow in women with elevated CRP further reinforces the link between maternal inflammation, impaired placental function, and restricted fetal growth [128]. Taken together, these data suggest that CRP reflects a systemic “read-out” of inflammatory pathways operating at both the maternal–fetal interface and in maternal circulation. Elevated CRP may therefore capture processes ranging from infection-related membrane weakening to placental insufficiency and cytokine-driven activation of labor. The stronger associations observed with hs-CRP assays and later gestational sampling in our subgroup analyses are consistent with the heightened role of low-grade inflammation and localized immune activation as pregnancy progresses.
Our review has several strengths. We included a large number of studies (n = 42), with an overall large, combined sample size, and incorporated both standard CRP and hs-CRP data, with stratified analyses by trimester, age, and CRP cut-offs. We also assessed risk of bias using NOS and certainty via GRADE and conducted sensitivity analyses. However, several limitations must also be noted. Included studies are observational and thus may be subject to residual confounding; many did not adjust fully for all potential confounders, as our NOS assessment showed. There is also very high heterogeneity in many pooled estimates (e.g., I2 over 80% in PTB and LBW), which may reflect differences in study design, CRP measurement, sample frames, and population characteristics. Publication bias was evident for some outcomes, meaning our effect estimates might be inflated. Another limitation is that all included studies measured maternal serum or plasma CRP, whereas evidence suggests that local inflammatory markers in amniotic fluid may provide stronger predictive value for adverse outcomes, highlighting that our review may underestimate the role of intrauterine inflammation. As this meta-analysis was limited to outcomes most consistently reported in the literature, fewer studies evaluated fetal growth restriction using standardized definitions; therefore, LBW and SGA were analyzed as growth-related outcomes, which may partially overlap with PTB and may not fully represent pathological fetal growth restriction. Finally, data on stillbirth and SGA remain sparse, limiting precision and certainty of evidence for those outcomes.
The findings of this systematic review and meta-analysis have important implications for clinical practice, policy, and future research. Clinically, routine measurement of maternal CRP—particularly using high-sensitivity assays—may aid in identifying pregnant women at increased risk of preterm birth and low birth weight, allowing for closer monitoring, early interventions, and potential use of anti-inflammatory or infection-targeted therapies. From a policy perspective, these results support integrating inflammatory biomarker screening into prenatal care guidelines, especially in high-risk populations, while emphasizing the need for standardized CRP thresholds and timing of assessment. For future research, large, well-controlled prospective studies and Mendelian randomization analyses are needed to clarify causal mechanisms, refine CRP cut-offs for clinical use, and explore whether modulation of maternal inflammation can improve neonatal outcomes. Additionally, investigation into local inflammatory markers in amniotic fluid may provide deeper insights into intrauterine processes driving adverse pregnancy outcomes.

5. Conclusions

In conclusion, this systematic review and meta-analysis provides evidence that elevated maternal CRP is associated with increased risks of PTB and LBW but does not appear reliably associated with SGA or stillbirth. These results support the potential of CRP as a maternal biomarker for identifying pregnancies at risk, especially when measured with hs-CRP in the later trimesters. Future research should prioritize well-designed prospective cohorts, harmonization of CRP measurement and cut-off points, and exploring interventions to reduce inflammation during pregnancy in order to improve neonatal outcomes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm15062114/s1. Supplementary File S1: The PRISMA checklist; Supplementary File S2: The search strategy [31,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72].; Supplementary File S3: Sensitivity analysis of the association between maternal c-reactive protein (CRP) levels and adverse neonatal outcomes: (A) standardized mean difference (SMD) of maternal CRP levels in overall adverse neonatal outcomes compared with controls, (B) odds of preterm birth in relation to elevated maternal CRP, (C) odds of low birth weight in relation to elevated maternal CRP, (D) odds of small for gestational age in relation to maternal CRP, and (E) odds of stillbirth in relation to maternal CRP; Supplementary File S4: Funnel plots assessing publication bias for the association between maternal CRP levels and adverse neonatal outcomes, with Egger’s and Begg’s test p-values reported for each outcome: (A) standardized mean difference (SMD) of maternal CRP levels in overall adverse neonatal outcomes compared with controls, Egger’s p = 0.022, Begg’s p = 0.334, (B) odds of preterm birth in relation to elevated maternal CRP, Egger’s p < 0.001, Begg’s p < 0.001, (C) odds of low birth weight in relation to elevated maternal CRP, Egger’s p < 0.001, Begg’s p = 0.072, (D) odds of small for gestational age in relation to maternal CRP, Egger’s p = 0.388, Begg’s p = 0.221, and (E) odds of stillbirth in relation to maternal CRP; Supplementary File S5: Certainty of evidence based on GRADE approach for the analysis of the association between maternal CRP levels and adverse neonatal outcomes.

Author Contributions

R.M., A.M.M. and A.A.-Z. contributed conceptualization, validation, formal analysis, supervision, and writing—original draft. A.A., F.S.A., R.I.A., L.A.S., M.B., G.A., L.T.A., M.A.A., N.S.A., N.A.A. and S.B. contributed to data curation, investigation, methodology, validation, visualization, and writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are available within the manuscript and its Supplementary Files.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bowman, C.E.; Arany, Z.; Wolfgang, M.J. Regulation of maternal–fetal metabolic communication. Cell. Mol. Life Sci. 2021, 78, 1455–1486. [Google Scholar] [CrossRef] [PubMed]
  2. Haddad-Tóvolli, R.; Claret, M. Metabolic and feeding adjustments during pregnancy. Nat. Rev. Endocrinol. 2023, 19, 564–580. [Google Scholar] [CrossRef] [PubMed]
  3. Cao, G.; Liu, J.; Liu, M. Global, regional, and national incidence and mortality of neonatal preterm birth, 1990–2019. JAMA Pediatr. 2022, 176, 787–796. [Google Scholar] [CrossRef] [PubMed]
  4. Rosa-Mangeret, F.; Benski, A.-C.; Golaz, A.; Zala, P.Z.; Kyokan, M.; Wagner, N.; Muhe, L.M.; Pfister, R.E. 2.5 million annual deaths—Are neonates in low-and middle-income countries too small to be seen? A bottom-up overview on neonatal morbi-mortality. Trop. Med. Infect. Dis. 2022, 7, 64. [Google Scholar] [CrossRef]
  5. Ohuma, E.O.; Moller, A.-B.; Bradley, E.; Chakwera, S.; Hussain-Alkhateeb, L.; Lewin, A.; Okwaraji, Y.B.; Mahanani, W.R.; Johansson, E.W.; Lavin, T. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: A systematic analysis. Lancet 2023, 402, 1261–1271. [Google Scholar] [CrossRef]
  6. Darmstadt, G.L.; Al Jaifi, N.H.; Arif, S.; Bahl, R.; Blennow, M.; Cavallera, V.; Chou, D.; Chou, R.; Comrie-Thomson, L.; Edmond, K. New World Health Organization recommendations for care of preterm or low birth weight infants: Health policy. EClinicalMedicine 2023, 63, 102155. [Google Scholar] [CrossRef]
  7. Okwaraji, Y.B.; Krasevec, J.; Bradley, E.; Conkle, J.; Stevens, G.A.; Gatica-Domínguez, G.; Ohuma, E.O.; Coffey, C.; Fernandez, D.G.E.; Blencowe, H. National, regional, and global estimates of low birthweight in 2020, with trends from 2000: A systematic analysis. Lancet 2024, 403, 1071–1080. [Google Scholar] [CrossRef]
  8. Chawla, D.; Agarwal, R. Preterm births and deaths: From counting to classification. Lancet Glob. Health 2022, 10, e1537–e1538. [Google Scholar] [CrossRef]
  9. Zahedi-Spung, L.D.; Raghuraman, N.; Macones, G.A.; Cahill, A.G.; Rosenbloom, J.I. Neonatal morbidity and mortality by mode of delivery in very preterm neonates. Am. J. Obstet. Gynecol. 2022, 226, 114.e1–114.e7. [Google Scholar] [CrossRef]
  10. Khazaei, Z.; Bagheri, M.M.; Goodarzi, E.; Moayed, L.; Abadi, N.E.; Bechashk, S.M.; Mohseni, S.; Safizadeh, M.; Behseresht, M.; Naghibzadeh-Tahami, A. Risk factors associated with low birth weight among infants: A nested case-control study in Southeastern Iran. Int. J. Prev. Med. 2021, 12, 159. [Google Scholar] [CrossRef]
  11. Moradi, G.; Zokaeii, M.; Goodarzi, E.; Khazaei, Z. Maternal risk factors for low birth weight infants: A nested case-control study of rural areas in Kurdistan (western of Iran). J. Prev. Med. Hyg. 2021, 62, E399. [Google Scholar] [PubMed]
  12. Grillo, M.A.; Mariani, G.; Ferraris, J.R. Prematurity and low birth weight in neonates as a risk factor for obesity, hypertension, and chronic kidney disease in pediatric and adult age. Front. Med. 2022, 8, 769734. [Google Scholar] [CrossRef] [PubMed]
  13. Bane, S.; Simard, J.F.; Wall-Wieler, E.; Butwick, A.J.; Carmichael, S.L. Subsequent risk of stillbirth, preterm birth, and small for gestational age: A cross-outcome analysis of adverse birth outcomes. Paediatr. Perinat. Epidemiol. 2022, 36, 815–823. [Google Scholar] [CrossRef] [PubMed]
  14. Wołejszo, S.; Genowska, A.; Motkowski, R.; Strukcinskiene, B.; Klukowski, M.; Konstantynowicz, J. Insights into prevention of health complications in small for gestational Age (SGA) births in relation to maternal characteristics: A narrative review. J. Clin. Med. 2023, 12, 531. [Google Scholar] [CrossRef]
  15. Sahin, R.; Tanacan, A.; Serbetci, H.; Agaoglu, Z.; Karagoz, B.; Haksever, M.; Kara, O.; Şahin, D. The role of first-trimester NLR (neutrophil to lymphocyte ratio), systemic immune-inflammation index (SII), and, systemic immune-response index (SIRI) in the prediction of composite adverse outcomes in pregnant women with systemic lupus erythematosus. J. Reprod. Immunol. 2023, 158, 103978. [Google Scholar] [CrossRef]
  16. Han, V.X.; Patel, S.; Jones, H.F.; Nielsen, T.C.; Mohammad, S.S.; Hofer, M.J.; Gold, W.; Brilot, F.; Lain, S.J.; Nassar, N. Maternal acute and chronic inflammation in pregnancy is associated with common neurodevelopmental disorders: A systematic review. Transl. Psychiatry 2021, 11, 71. [Google Scholar] [CrossRef]
  17. Hashemipour, S.; Kalantarian, S.S.; Panahi, H.; Kelishomi, S.E.; Ghasemi, A.; Chopani, S.M.; Kolaji, S.; Badri, M.; Ghobadi, A.; Khairkhahan, S.M.R.H. The association of inflammatory markers in early pregnancy with the development of gestational diabetes: Qazvin maternal and neonatal metabolic study (QMNS). BMC Pregnancy Childbirth 2025, 25, 135. [Google Scholar] [CrossRef]
  18. Morelli, S.S.; Mandal, M.; Goldsmith, L.T.; Kashani, B.N.; Ponzio, N.M. The maternal immune system during pregnancy and its influence on fetal development. Res. Rep. Biol. 2015, 6, 171–189. [Google Scholar] [CrossRef]
  19. Fragoso, M.B.T.; Ferreira, R.C.; Tenório, M.C.d.S.; Moura, F.A.; de Araújo, O.R.P.; Bueno, N.B.; Goulart, M.O.F.; de Oliveira, A.C.M. Biomarkers of inflammation and redox imbalance in umbilical cord in pregnancies with and without preeclampsia and consequent perinatal outcomes. Oxidative Med. Cell. Longev. 2021, 2021, 9970627. [Google Scholar] [CrossRef]
  20. Dockree, S.; Brook, J.; James, T.; Shine, B.; Impey, L.; Vatish, M. Pregnancy-specific reference intervals for C-reactive protein improve diagnostic accuracy for infection: A longitudinal study. Clin. Chim. Acta 2021, 517, 81–85. [Google Scholar] [CrossRef]
  21. Stanimirovic, J.; Radovanovic, J.; Banjac, K.; Obradovic, M.; Essack, M.; Zafirovic, S.; Gluvic, Z.; Gojobori, T.; Isenovic, E.R. Role of C-reactive protein in diabetic inflammation. Mediat. Inflamm. 2022, 2022, 3706508. [Google Scholar] [CrossRef] [PubMed]
  22. Mertens, K.; Muys, J.; Jacquemyn, Y. Postpartum C-reactive protein: A limited value to detect infection or inflammation. Facts Views Vis. ObGyn 2020, 11, 243. [Google Scholar]
  23. Amirian, A.; Rahnemaei, F.A.; Abdi, F. Role of C-reactive Protein (CRP) or high-sensitivity CRP in predicting gestational diabetes Mellitus: Systematic review. Diabetes Metab. Syndr. Clin. Res. Rev. 2020, 14, 229–236. [Google Scholar] [CrossRef] [PubMed]
  24. Rahnemaei, F.A.; Abdi, F. C-reactive protein or high-sensitivity CRP as a predictor for gestational diabetes: Recommended or not? J. Obstet. Gynaecol. 2021, 41, 495. [Google Scholar] [CrossRef]
  25. Ates, M.; Calis, P.; Artiktay, A.G.; Yilmaz, C.; Hirfanoglu, I.M.; Erdem, A.; Erdem, M. Associations of CRP and PCT levels with obstetric and neonatal outcomes: A prospective study. BMC Pregnancy Childbirth 2025, 25, 873. [Google Scholar] [CrossRef]
  26. Nikbakht, R.; Moghadam, E.K.; Nasirkhani, Z. Maternal serum levels of C-reactive protein at early pregnancy to predict fetal growth restriction and preterm delivery: A prospective cohort study. Int. J. Reprod. Biomed. 2020, 18, 157. [Google Scholar] [CrossRef]
  27. Park, H.; Park, K.H.; Kim, Y.M.; Kook, S.Y.; Jeon, S.J.; Yoo, H.-N. Plasma inflammatory and immune proteins as predictors of intra-amniotic infection and spontaneous preterm delivery in women with preterm labor: A retrospective study. BMC Pregnancy Childbirth 2018, 18, 146. [Google Scholar] [CrossRef]
  28. Huang, S.; Tian, J.; Liu, C.; Long, Y.; Cao, D.; Wei, L.; Zhu, X.; Tang, R.; Liu, W.; Zeng, D. Elevated C-reactive protein and complement C3 levels are associated with preterm birth: A nested case–control study in Chinese women. BMC Pregnancy Childbirth 2020, 20, 131. [Google Scholar] [CrossRef]
  29. Sadiq, A.M.; Hussein, C.M.; Yousif, M.; Mohammed, R. Correlation Between Highly Sensitive C-Reactive Protein Level in Cases of Preeclampsia with or without Intrauterine-Growth Restriction. Indian J. Forensic Med. Toxicol. 2020, 17, 2–4. [Google Scholar] [CrossRef]
  30. Kara, A.E.; Guney, G.; Tokmak, A.; Ozaksit, G. The role of inflammatory markers hs-CRP, sialic acid, and IL-6 in the pathogenesis of preeclampsia and intrauterine growth restriction. Eur. Cytokine Netw. 2019, 30, 29–33. [Google Scholar] [CrossRef]
  31. Witteveen, A.B.; Henrichs, J.; Bellers, M.; van Oenen, E.; Verhoeven, C.J.; Vrijkotte, T.G. Mediating role of C-reactive protein in associations between pre-pregnancy BMI and adverse maternal and neonatal outcomes: The ABCD-study cohort. J. Matern.-Fetal Neonatal Med. 2022, 35, 2867–2875. [Google Scholar] [CrossRef]
  32. Ernst, G.D.; De Jonge, L.L.; Hofman, A.; Lindemans, J.; Russcher, H.; Steegers, E.A.; Jaddoe, V.W. C-reactive protein levels in early pregnancy, fetal growth patterns, and the risk for neonatal complications: The Generation R Study. Am. J. Obstet. Gynecol. 2011, 205, 132.e1–132.e12. [Google Scholar] [CrossRef] [PubMed]
  33. Couture, C.; Brien, M.-E.; Boufaied, I.; Duval, C.; Dal Soglio, D.; Enninga, E.A.L.; Cox, B.; Girard, S. Proinflammatory changes in the maternal circulation, maternal–fetal interface, and placental transcriptome in preterm birth. Am. J. Obstet. Gynecol. 2023, 228, 332.e1–332.e17. [Google Scholar] [CrossRef] [PubMed]
  34. Hutton, B.; Catalá-López, F.; Moher, D. La extensión de la declaración PRISMA para revisiones sistemáticas que incorporan metaanálisis en red: PRISMA-NMA. Med. Clínica 2016, 147, 262–266. [Google Scholar] [CrossRef] [PubMed]
  35. Brooke, B.S.; Schwartz, T.A.; Pawlik, T.M. MOOSE reporting guidelines for meta-analyses of observational studies. JAMA Surg. 2021, 156, 787–788. [Google Scholar] [CrossRef]
  36. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, 71. [Google Scholar] [CrossRef]
  37. Quinn, J.-A.; Munoz, F.M.; Gonik, B.; Frau, L.; Cutland, C.; Mallett-Moore, T.; Kissou, A.; Wittke, F.; Das, M.; Nunes, T. Preterm birth: Case definition & guidelines for data collection, analysis, and presentation of immunisation safety data. Vaccine 2016, 34, 6047–6056. [Google Scholar] [CrossRef]
  38. Hughes, M.M.; Black, R.E.; Katz, J. 2500-g low birth weight cutoff: History and implications for future research and policy. Matern. Child Health J. 2017, 21, 283–289. [Google Scholar] [CrossRef]
  39. Damhuis, S.E.; Ganzevoort, W.; Gordijn, S.J. Abnormal fetal growth: Small for gestational age, fetal growth restriction, large for gestational age: Definitions and epidemiology. Obstet. Gynecol. Clin. N. Am. 2021, 48, 267–279. [Google Scholar] [CrossRef]
  40. Kelly, K.; Meaney, S.; Leitao, S.; O’Donoghue, K. A review of stillbirth definitions: A rationale for change. Eur. J. Obstet. Gynecol. Reprod. Biol. 2021, 256, 235–245. [Google Scholar] [CrossRef]
  41. Wells, G.A.; Shea, B.; O’Connell, D.; Peterson, J.; Welch, V.; Losos, M.; Tugwell, P. The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. 2000. Available online: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp (accessed on 18 July 2025).
  42. Guyatt, G.H.; Oxman, A.D.; Vist, G.E.; Kunz, R.; Falck-Ytter, Y.; Alonso-Coello, P.; Schünemann, H.J. GRADE: An emerging consensus on rating quality of evidence and strength of recommendations. BMJ 2008, 336, 924–926. [Google Scholar] [CrossRef]
  43. DerSimonian, R.; Laird, N. Meta-analysis in clinical trials. Control. Clin. Trials 1986, 7, 177–188. [Google Scholar] [CrossRef] [PubMed]
  44. Higgins, J.P.; Thompson, S.G.; Deeks, J.J.; Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 2003, 327, 557–560. [Google Scholar] [CrossRef] [PubMed]
  45. Antoniou, M.-C.; Quansah, D.Y.; Gilbert, L.; Arhab, A.; Schenk, S.; Lacroix, A.; Stuijfzand, B.; Horsch, A.; Puder, J.J. Association between maternal and fetal inflammatory biomarkers and offspring weight and BMI during the first year of life in pregnancies with GDM: MySweetheart study. Front. Endocrinol. 2024, 15, 1333755. [Google Scholar] [CrossRef] [PubMed]
  46. Bucak, M.; Seyhanli, Z.; Cakir, B.T.; Ulusoy, C.O.; Karabay, G.; Aktemur, G.; Akkus, F.; Yılmaz, Z.V. The role of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP) and fibrinogen in predicting the latent period after preterm premature rupture of membranes between 24 and 34 weeks. Perinat. J. 2024, 32, 216–225. [Google Scholar]
  47. Chen, Y.-C.S.; Mirzakhani, H.; Knihtilä, H.; Fichorova, R.N.; Luu, N.; Laranjo, N.; Jha, A.; Kelly, R.S.; Weiss, S.T.; Litonjua, A.A. The association of prenatal C-reactive protein and interleukin-8 levels with maternal characteristics and preterm birth. Am. J. Perinatol. 2024, 41, e843–e852. [Google Scholar]
  48. Christensen, S.H.; Rom, A.L.; Greve, T.; Lewis, J.I.; Frøkiær, H.; Allen, L.H.; Mølgaard, C.; Renault, K.M.; Michaelsen, K.F. Maternal inflammatory, lipid and metabolic markers and associations with birth and breastfeeding outcomes. Front. Nutr. 2023, 10, 1223753. [Google Scholar] [CrossRef]
  49. Gogeneni, H.; Buduneli, N.; Ceyhan-Öztürk, B.; Gümüş, P.; Akcali, A.; Zeller, I.; Renaud, D.E.; Scott, D.A.; Özçaka, Ö. Increased infection with key periodontal pathogens during gestational diabetes mellitus. J. Clin. Periodontol. 2015, 42, 506–512. [Google Scholar] [CrossRef]
  50. Kelly, R.S.; Lee-Sarwar, K.; Chen, Y.-C.; Laranjo, N.; Fichorova, R.; Chu, S.H.; Prince, N.; Lasky-Su, J.; Weiss, S.T.; Litonjua, A.A. Maternal inflammatory biomarkers during pregnancy and early life neurodevelopment in offspring: Results from the VDAART study. Int. J. Mol. Sci. 2022, 23, 15249. [Google Scholar] [CrossRef]
  51. Khairnar, M.S.; Pawar, B.R.; Marawar, P.P.; Khairnar, D.M. Estimation of changes in C-reactive protein level and pregnancy outcome after nonsurgical supportive periodontal therapy in women affected with periodontitis in a rural set up of India. Contemp. Clin. Dent. 2015, 6, S5–S11. [Google Scholar] [CrossRef]
  52. Chen, J.; Navais, P.S.; Xu, H.; Flatley, C.; Bacelis, J.; Monangi, N.; Kacerovsky, M.; Hallman, M.; Teramo, K.; Lawlor, D.A. Interrogating the causal effects of maternal circulating CRP on gestational duration and birth weight. medRxiv 2022. [Google Scholar] [CrossRef]
  53. Hackney, D.N.; Macpherson, T.A.; Dunigan, J.T.; Simhan, H.N. First-trimester maternal plasma concentrations of C-reactive protein in low-risk patients and the subsequent development of chorioamnionitis. Am. J. Perinatol. 2008, 25, 407–411. [Google Scholar] [CrossRef] [PubMed]
  54. Hastie, C.E.; Smith, G.C.; Mackay, D.F.; Pell, J.P. Association between preterm delivery and subsequent C-reactive protein: A retrospective cohort study. Am. J. Obstet. Gynecol. 2011, 205, 556. e551–556. e554. [Google Scholar] [CrossRef] [PubMed]
  55. James, U.A.; Imaralu, J.O.; Esiaba, I. Evaluation of Serum Interleukin-6 and C-Reactive Protein Levels among Women During Term Labour. J. Adv. Med. Med. Res. 2020, 32, 69–76. [Google Scholar] [CrossRef]
  56. Kidd, M.G.; McDade, T.W. Association between C-reactive protein response to influenza vaccine during pregnancy and birth outcomes. Am. J. Hum. Biol. 2022, 34, e23569. [Google Scholar] [CrossRef]
  57. Kim, H.; Hwang, J.; Ha, E.; Park, H.; Ha, M.; Lee, S.; Hong, Y.; Chang, N. Association of maternal folate nutrition and serum C-reactive protein concentrations with gestational age at delivery. Eur. J. Clin. Nutr. 2011, 65, 350–356. [Google Scholar] [CrossRef]
  58. Kyaw, E.M.M.; San, C. Association Between Maternal Serum C-Reactive Protein in Early Pregnancy and Spontaneous Preterm Delivery: A Prospective Hospital-Based Study in Yangon, Myanmar. Res. J. Pharm. Technol. 2025, 18, 4999–5002. [Google Scholar] [CrossRef]
  59. Manoppo, M.; Tendean, H.M.; Sondakh, J.M. High Sensitivity CReactive Protein (hsCRP) Level on Premature Rupture of Membrane (PROM) at Term Pregnancy. Indones. J. Obstet. Gynecol. 2017, 5, 12–15. [Google Scholar] [CrossRef]
  60. Ng, P.; Cheng, S.; Chui, K.; Fok, T.; Wong, M.; Wong, W.; Wong, R.; Cheung, K. Diagnosis of late onset neonatal sepsis with cytokines, adhesion molecule, and C-reactive protein in preterm very low birthweight infants. Arch. Dis. Child.-Fetal Neonatal Ed. 1997, 77, F221–F227. [Google Scholar] [CrossRef]
  61. Parvatikar, S. A Comparative Study of Serum Uric Acid C Reactive Protein and Serum Calcium in Preeclampsia and Normal Pregnancy; Rajiv Gandhi University of Health Sciences (India): Karnataka, India, 2013. [Google Scholar]
  62. Paul, K.; Boutain, D.; Agnew, K.; Thomas, J.; Hitti, J. The relationship between racial identity, income, stress and C-reactive protein among parous women: Implications for preterm birth disparity research. J. Natl. Med. Assoc. 2008, 100, 540–546. [Google Scholar] [CrossRef]
  63. Pieczyńska, J.; Płaczkowska, S.; Pawlik-Sobecka, L.; Kokot, I.; Sozański, R.; Grajeta, H. Association of dietary inflammatory index with serum IL-6, IL-10, and CRP concentration during pregnancy. Nutrients 2020, 12, 2789. [Google Scholar] [CrossRef]
  64. Keenan-Devlin, L.S.; Caplan, M.; Freedman, A.; Kuchta, K.; Grobman, W.; Buss, C.; Adam, E.K.; Entringer, S.; Miller, G.E.; Borders, A.E. Using principal component analysis to examine associations of early pregnancy inflammatory biomarker profiles and adverse birth outcomes. Am. J. Reprod. Immunol. 2021, 86, e13497. [Google Scholar] [CrossRef] [PubMed]
  65. Ryu, H.K.; Moon, J.H.; Heo, H.J.; Kim, J.W.; Kim, Y.H. Maternal c-reactive protein and oxidative stress markers as predictors of delivery latency in patients experiencing preterm premature rupture of membranes. Int. J. Gynecol. Obstet. 2017, 136, 145–150. [Google Scholar] [CrossRef] [PubMed]
  66. Chul Sung, K.; Suh, J.Y.; Kim, B.S.; Kang, J.H.; Kim, H.; Lee, M.H.; Park, J.R.; Kim, S.W. High sensitivity C-reactive protein as an independent risk factor for essential hypertension. Am. J. Hypertens. 2003, 16, 429–433. [Google Scholar] [CrossRef] [PubMed]
  67. Ali, M.; Hameed, B.; Kamel, W. The association of serum cancer antigen 125 and c–reactive protein level with the severity of preeclampsia. Karbala J. Med. 2012, 5, 1322–1328. [Google Scholar]
  68. Seyhanli, Z.; Bayraktar, B.; Cakir, B.T.; Bucak, M.; Karabay, G.; Aktemur, G.; Yigit, A.; Yucel, K.Y.; Yılmaz, Z.V. The Efficacy of C-Reactive Protein (CRP) to Albumin Ratio (CAR) and Fibrinogen to CRP Ratio (FCR) in Predicting the Latent Period of Preterm Labor. Am. J. Reprod. Immunol. 2024, 92, e13899. [Google Scholar] [CrossRef]
  69. Shafiq, M.; Mathad, J.S.; Naik, S.; Alexander, M.; Yadana, S.; Araújo-Pereira, M.; Kulkarni, V.; Deshpande, P.; Kumar, N.P.; Babu, S. Association of maternal inflammation during pregnancy with birth outcomes and infant growth among women with or without HIV in India. JAMA Netw. Open 2021, 4, e2140584. [Google Scholar] [CrossRef]
  70. Song, J.S.; Woo, S.J.; Park, K.H.; Kim, H.; Lee, K.-N.; Kim, Y.M. Association of inflammatory and angiogenic biomarkers in maternal plasma with retinopathy of prematurity in preterm infants. Eye 2023, 37, 1802–1809. [Google Scholar] [CrossRef]
  71. Suwardewa, T.G.A.; Sanjaya, I.N.H.; Anantasika, A.A.N.; Aryana, M.B.D.; Widiyanti, E.S.; Kurniawan, P.I. Correlation between group B Streptococcus infection in the vagina with maternal serum C-reactive protein levels in preterm labor. Eur. J. Med. Health Sci. 2022, 4, 18–21. [Google Scholar] [CrossRef]
  72. Yeates, A.J.; McSorley, E.M.; Mulhern, M.S.; Spence, T.; Crowe, W.; Grzesik, K.; Thurston, S.; Watson, G.; Myers, G.; Davidson, P. Associations between maternal inflammation during pregnancy and infant birth outcomes in the Seychelles Child Development Study. J. Reprod. Immunol. 2020, 137, 102623. [Google Scholar] [CrossRef]
  73. Bakalis, S.P.; Poon, L.C.; Vayna, A.-M.; Pafilis, I.; Nicolaides, K.H. C-reactive protein at 11–13 weeks’ gestation in spontaneous early preterm delivery. J. Matern.-Fetal Neonatal Med. 2012, 25, 2475–2478. [Google Scholar] [CrossRef] [PubMed]
  74. Biswas, J.; Datta, M.; Kar, K.; Mitra, D.; Jyothi, L.; Maitra, A. Role of serum high-sensitive C-reactive protein to predict severity of pre-eclampsia in a high-population resource-poor country: A prospective observational study. J. Rural. Med. 2025, 20, 71–77. [Google Scholar] [CrossRef] [PubMed]
  75. Bullen, B.L.; Jones, N.M.; Holzman, C.B.; Tian, Y.; Senagore, P.K.; Thorsen, P.; Skogstrand, K.; Hougaard, D.M.; Sikorskii, A. C-reactive protein and preterm delivery: Clues from placental findings and maternal weight. Reprod. Sci. 2013, 20, 715–722. [Google Scholar] [CrossRef] [PubMed]
  76. Chen, Y.-Y.; Zhou, Y.-B.; Yang, J.; Hua, Y.-M.; Yuan, P.-B.; Liu, A.-P.; Wei, Y. Serum hsCRP in early pregnancy and preterm delivery in twin gestations: A prospective cohort study. BMC Pregnancy Childbirth 2023, 23, 123. [Google Scholar] [CrossRef]
  77. Cohen, Y.; Ascher-Landsberg, J.; Cohen, A.; Lessing, J.B.; Grisaru, D. The role of C-reactive protein measurement as a diagnostic aid in early pregnancy. Eur. J. Obstet. Gynecol. Reprod. Biol. 2014, 176, 64–67. [Google Scholar] [CrossRef]
  78. de Oliveira, L.C.; Franco-Sena, A.B.; Farias, D.R.; Rebelo, F.; Kac, G. Maternal C-reactive protein concentrations during pregnancy and birth weight in a prospective cohort in Rio de Janeiro, Brazil. J. Matern.-Fetal Neonatal Med. 2017, 30, 2346–2353. [Google Scholar] [CrossRef]
  79. Deo, S.; Jaiswar, S.; Sankhwar, P.; Kumari, P.; Singh, S. Evaluation of CRP as a preindicative marker in women with Preterm Labour and Preterm Prelabour Rupture of Membrane (PPROM). Int. J. Life-Sci. Sci. Res. 2016, 2, 466–471. [Google Scholar] [CrossRef]
  80. Erkenekli, K.; Keskin, U.; Uysal, B.; Kurt, Y.; Sadir, S.; Çayci, T.; Ergün, A.; Erkaya, S.; Danişman, N.; Uygur, D. Levels of neopterin and C-reactive protein in pregnant women with fetal growth restriction. J. Obstet. Gynaecol. 2015, 35, 225–228. [Google Scholar] [CrossRef]
  81. Fischer-Suárez, N.; Fernández-Alonso, A.M.; Herrera-Muñoz, A.; Chedraui, P.; Pérez-López, F.R. Maternal serum 25-hydroxyvitamin D and C-reactive protein levels in pregnancies complicated with threatened preterm labour. Gynecol. Endocrinol. 2016, 32, 777–781. [Google Scholar] [CrossRef]
  82. Gahlot, K.; Pandey, K.; Singh, P.P.; Gahlot, V.; Mourya, R. To evaluate diagnostic efficacy of maternal serum C-reactive protein to predict preterm labour. Int. J. Reprod. Contracept. Obstet. Gynecol. 2016, 5, 4001–4004. [Google Scholar] [CrossRef]
  83. Haedersdal, S.; Salvig, J.D.; Aabye, M.; Thorball, C.W.; Ruhwald, M.; Ladelund, S.; Eugen-Olsen, J.; Secher, N.J. Inflammatory markers in the second trimester prior to clinical onset of preeclampsia, intrauterine growth restriction, and spontaneous preterm birth. Inflammation 2013, 36, 907–913. [Google Scholar] [CrossRef] [PubMed]
  84. Halder, A.; Agarwal, R.; Sharma, S.; Agarwal, S. Predictive significance of C reactive protein in spontaneous preterm delivery: A prospective cohort study. Int. J. Reprod. Contracept. Obstet. Gynecol. 2013, 2, 47–51. [Google Scholar] [CrossRef]
  85. Jyothi, V.J.; Farheen, S.; Deepthi, M.; Swapna, L. Evaluation of screening efficacy of IL6, IL8, CRP and salivary progesterone in predicting preterm pregnancy. Int. J. Reprod. Contracept. Obstet. Gynecol. 2023, 12, 941. [Google Scholar] [CrossRef]
  86. Karlı, P.; Özdemir, A.Z.; Ayan, D. Maternal serum and fetal cord blood C-reactive protein levels but not procalcitonin levels are increased in idiopathic intrauterine growth restriction. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2019, 25, 6512. [Google Scholar] [CrossRef]
  87. Kennelly, M.A.; Killeen, S.L.; Phillips, C.M.; Alberdi, G.; Lindsay, K.L.; Mehegan, J.; Cronin, M.; McAuliffe, F.M. Maternal C3 complement and C-reactive protein and pregnancy and fetal outcomes: A secondary analysis of the PEARS RCT-An mHealth-supported, lifestyle intervention among pregnant women with overweight and obesity. Cytokine 2022, 149, 155748. [Google Scholar] [CrossRef]
  88. Kirici, P.; Çağıran, F.; Kali, Z.; Tanriverdi, E.; Mavral, N.; Ecin, S. Determination of maternal serum pro-inflammatory cytokine changes in intrauterine growth restriction. Eur. Rev. Med. Pharmacol. Sci. 2023, 27, 1996–2001. [Google Scholar]
  89. Kolelupun, M.; Surya, I.G.P.; Sanjaya, I.N.H.; Suwardewa, T.G.A.; Megadhana, I.W.; Putra, I.G.M.; Budiana, I.N.G.; Putra, I.W.A. High level of highly sensitivity c-reactive protein levels (hs-CRP) as a risk factor for preterm delivery. Bali Med. J. 2022, 11, 40–43. [Google Scholar] [CrossRef]
  90. Koleva-Korkelia, I.; Karamalakova, Y. C-reactive protein levels-indicator for prognosis of spontaneous preterm birth in bulgarian women. Proc. CBU Med. Pharm. 2021, 2, 80. [Google Scholar] [CrossRef]
  91. Lohsoonthorn, V.; Qiu, C.; Williams, M.A. Maternal serum C-reactive protein concentrations in early pregnancy and subsequent risk of preterm delivery. Clin. Biochem. 2007, 40, 330–335. [Google Scholar] [CrossRef]
  92. Madjid, T.H.; Prasetyawati, R.D.; Nathania, N.; Iswari, W.A.; Aziz, M.A.; Pusianawati, D.; Effendi, J.S. C-reactive protein concentration in very early, early and late preterm labour. Indones. J. Obstet. Gynecol. Sci. 2020, 3, 99–105. [Google Scholar] [CrossRef]
  93. Mahapatra, A.; Nayak, R.; Satpathy, A.; Pati, B.K.; Mohanty, R.; Mohanty, G.; Beura, R. Maternal periodontal status, oral inflammatory load, and systemic inflammation are associated with low infant birth weight. J. Periodontol. 2021, 92, 1107–1116. [Google Scholar] [CrossRef] [PubMed]
  94. Mannava, P.; Gokhale, S.; Pujari, S.; Biswas, K.P.; Kaliappan, S.; Vijapure, S. Comparative evaluation of C-reactive Proteins in pregnant women with and without periodontal pathologies: A prospective cohort analysis. J. Contemp. Dent. Pract. 2016, 17, 480–483. [Google Scholar] [CrossRef] [PubMed]
  95. Mansor, A.E.; Farag, A.M. The predictve value of Early Second Trimester C-Reactive Protein for detection of preterm labour and neonatal outcome. MJMR (Minia J. Med. Res.) 2018, 29, 238–242. [Google Scholar]
  96. Moghaddam Banaem, L.; Mohamadi, B.; Asghari Jaafarabadi, M.; Aliyan Moghadam, N. Maternal serum C-reactive protein in early pregnancy and occurrence of preterm premature rupture of membranes and preterm birth. J. Obstet. Gynaecol. Res. 2012, 38, 780–786. [Google Scholar] [CrossRef]
  97. Nakishbandy, B.M.N.; Barawi, S.A. Level of C-reactive protein as an indicator for prognosis of premature uterine contractions. J. Prenat. Med. 2014, 8, 25. [Google Scholar]
  98. Ozgu-Erdinc, A.S.; Cavkaytar, S.; Aktulay, A.; Buyukkagnici, U.; Erkaya, S.; Danisman, N. Mid-trimester maternal serum and amniotic fluid biomarkers for the prediction of preterm delivery and intrauterine growth retardation. J. Obstet. Gynaecol. Res. 2014, 40, 1540–1546. [Google Scholar] [CrossRef]
  99. Pitiphat, W.; Gillman, M.W.; Joshipura, K.J.; Williams, P.L.; Douglass, C.W.; Rich-Edwards, J.W. Plasma C-reactive protein in early pregnancy and preterm delivery. Am. J. Epidemiol. 2005, 162, 1108–1113. [Google Scholar] [CrossRef]
  100. Reroń, A.; Huras, H.; Szymik, M.; Jaworowski, A. C-reactive protein as a predictor of threatening preterm delivery. Neuroendocrinol. Lett. 2004, 25, 302–306. [Google Scholar]
  101. Rezaei, M.; Shahgheibi, S.; Shahoei, R.; Zadvakili, F.; Farhadifar, F.; Noori, N.; Saiedalshoiadaei, F. Cervicovaginal biomarkers and C-reactive protein levels in preterm and term labor. Life Sci. J. 2013, 10, 368–371. [Google Scholar]
  102. Rzepka, R.; Dołęgowska, B.; Rajewska, A.; Sałata, D.; Budkowska, M.; Kwiatkowski, S.; Torbé, A. Diagnostic Potential of Evaluation of SDF-1α and sRAGE Levels in Threatened Premature Labor. BioMed Res. Int. 2016, 2016, 2719460. [Google Scholar] [CrossRef]
  103. Savvidou, M.D.; Lees, C.C.; Parra, M.; Hingorani, A.D.; Nicolaides, K.H. Levels of C-reactive protein in pregnant women who subsequently develop pre-eclampsia. BJOG Int. J. Obstet. Gynaecol. 2002, 109, 297–301. [Google Scholar]
  104. Shahshahan, Z.; Rasouli, O. The use of maternal C-reactive protein in the predicting of preterm labor and tocolytic therapy in preterm labor women. Adv. Biomed. Res. 2014, 3, 154. [Google Scholar] [CrossRef] [PubMed]
  105. Sharma, A.; Ramesh, A.; Thomas, B. Evaluation of plasma C-reactive protein levels in pregnant women with and without periodontal disease: A comparative study. J. Indian Soc. Periodontol. 2009, 13, 145–149. [Google Scholar] [CrossRef] [PubMed]
  106. Sinha, S.; Seth, S.; Rai, P. Study of Cervical Mucus Cytology and Serum Inflammatory Biomarkers in Preterm Labour Compared to Term Pregnancy Cases in a Tertiary Care Center of Western Uttar Pradesh (UP), India. Cureus 2023, 15, e48884. [Google Scholar] [CrossRef] [PubMed]
  107. Sorokin, Y.; Romero, R.; Mele, L.; Wapner, R.J.; Iams, J.D.; Dudley, D.J.; Spong, C.Y.; Peaceman, A.M.; Leveno, K.J.; Harper, M. Maternal serum interleukin-6, C-reactive protein, and matrix metalloproteinase-9 concentrations as risk factors for preterm birth <32 weeks and adverse neonatal outcomes. Am. J. Perinatol. 2010, 27, 631–640. [Google Scholar]
  108. Vijetha, K.L.; Sujatha, P.; Kate, N.V.; Vaithiyanadhan, U.; Gopal, V. Correlation of levels of serum C reactive protein in second trimester with fetomaternal outcome. Int. J. Reprod. Contracept. Obstet. Gynecol. 1930, 11, 865. [Google Scholar] [CrossRef]
  109. Yang, Y.; Kan, H.; Yu, X.; Yang, Y.; Li, L.; Zhao, M. Relationship between dietary inflammatory index, hs-CRP level in the second trimester and neonatal birth weight: A cohort study. J. Clin. Biochem. Nutr. 2020, 66, 163–167. [Google Scholar] [CrossRef]
  110. Rebelo, F.; Schluessel, M.M.; Vaz, J.S.; Franco-Sena, A.B.; Pinto, T.J.; Bastos, F.I.; Adegboye, A.R.; Kac, G. C-reactive protein and later preeclampsia: Systematic review and meta-analysis taking into account the weight status. J. Hypertens. 2013, 31, 16–26. [Google Scholar] [CrossRef]
  111. Hamadeh, R.; Mohsen, A.; Kobeissy, F.; Karouni, A.; Akoum, H. C-reactive protein for prediction or early detection of pre-eclampsia: A systematic review. Gynecol. Obstet. Investig. 2021, 86, 13–26. [Google Scholar] [CrossRef]
  112. Zhang, J.; Luo, W.; Huang, P.; Peng, L.; Huang, Q. Maternal C-reactive protein and cytokine levels during pregnancy and the risk of selected neuropsychiatric disorders in offspring: A systematic review and meta-analysis. J. Psychiatr. Res. 2018, 105, 86–94. [Google Scholar] [CrossRef]
  113. Lucaroni, F.; Morciano, L.; Rizzo, G.; D’Antonio, F.; Buonuomo, E.; Palombi, L.; Arduini, D. Biomarkers for predicting spontaneous preterm birth: An umbrella systematic review. J. Matern.-Fetal Neonatal Med. 2018, 31, 726–734. [Google Scholar] [CrossRef] [PubMed]
  114. Wei, S.-Q.; Fraser, W.; Luo, Z.-C. Inflammatory cytokines and spontaneous preterm birth in asymptomatic women: A systematic review. Obstet. Gynecol. 2010, 116, 393–401. [Google Scholar] [CrossRef] [PubMed]
  115. van de Laar, R.; van der Ham, D.P.; Oei, S.G.; Willekes, C.; Weiner, C.P.; Mol, B.W. Accuracy of C-reactive protein determination in predicting chorioamnionitis and neonatal infection in pregnant women with premature rupture of membranes: A systematic review. Eur. J. Obstet. Gynecol. Reprod. Biol. 2009, 147, 124–129. [Google Scholar] [CrossRef]
  116. Brown, A.S.; Sourander, A.; Hinkka-Yli-Salomäki, S.; McKeague, I.W.; Sundvall, J.; Surcel, H.-M. Elevated maternal C-reactive protein and autism in a national birth cohort. Mol. Psychiatry 2014, 19, 259–264. [Google Scholar] [CrossRef]
  117. Chudal, R.; Brown, A.S.; Gyllenberg, D.; Hinkka-Yli-Salomäki, S.; Sucksdorff, M.; Surcel, H.-M.; Upadhyaya, S.; Sourander, A. Maternal serum C-reactive protein (CRP) and offspring attention deficit hyperactivity disorder (ADHD). Eur. Child Adolesc. Psychiatry 2020, 29, 239–247. [Google Scholar] [CrossRef]
  118. Humberg, A.; Fortmann, I.; Siller, B.; Kopp, M.V.; Herting, E.; Göpel, W.; Härtel, C. German Neonatal Network; German Center for Lung Research and Priming Immunity at the beginning of life (PRIMAL) Consortium. Preterm birth and sustained inflammation: Consequences for the neonate. Semin. Immunopathol. 2020, 42, 451–468. [Google Scholar] [CrossRef]
  119. Romero, R.; Espinoza, J.; Gonçalves, L.F.; Kusanovic, J.P.; Friel, L.; Hassan, S. The role of inflammation and infection in preterm birth. Semin. Reprod. Med. 2007, 25, 21–39. [Google Scholar] [CrossRef]
  120. Socha, M.W.; Flis, W.; Pietrus, M.; Wartęga, M.; Stankiewicz, M. Signaling pathways regulating human cervical ripening in preterm and term delivery. Cells 2022, 11, 3690. [Google Scholar] [CrossRef]
  121. Adu-Bonsaffoh, K.; Bayor, F. Pathophysiological mechanisms of maternal pro-inflammatory mediators in preterm labour. J. Physiol. Pathophysiol. 2022, 13, 1–16. [Google Scholar] [CrossRef]
  122. Adan, R.; Özdemir, S.; Şahin, F.; Ömeroğlu, İ.; Aşıcıoğlu, O. The Association Between C-Reactive Protein and the Duration of the Latent Phase of Labor in Women with Term Premature Rupture of Membranes. Anatol. J. Obstet. Gynecol. Res. 2025, 1, 104–108. [Google Scholar] [CrossRef]
  123. Parisi, F.; Milazzo, R.; Savasi, V.M.; Cetin, I. Maternal low-grade chronic inflammation and intrauterine programming of health and disease. Int. J. Mol. Sci. 2021, 22, 1732. [Google Scholar] [CrossRef]
  124. Chen, X.; Zhang, M.; Zhou, N.; Zhou, W.; Qi, H. Associations between genetically predicted concentrations of circulating inflammatory cytokines and the risk of ten pregnancy-related adverse outcomes: A two-sample Mendelian randomization study. Cytokine 2024, 180, 156661. [Google Scholar] [CrossRef]
  125. Garciéa, R.G.; Celedón, J.; Sierra-Laguado, J.; Alarcón, M.A.; Luengas, C.; Silva, F.; Arenas-Mantilla, M.; López-Jaramillo, P. Raised C-reactive protein and impaired flow-mediated vasodilation precede the development of preeclampsia. Am. J. Hypertens. 2007, 20, 98–103. [Google Scholar] [CrossRef] [PubMed]
  126. Weckman, A.M.; Ngai, M.; Wright, J.; McDonald, C.R.; Kain, K.C. The impact of infection in pregnancy on placental vascular development and adverse birth outcomes. Front. Microbiol. 2019, 10, 1924. [Google Scholar] [CrossRef]
  127. Beltempo, M.; Viel-Thériault, I.; Thibeault, R.; Julien, A.-S.; Piedboeuf, B. C-reactive protein for late-onset sepsis diagnosis in very low birth weight infants. BMC Pediatr. 2018, 18, 16. [Google Scholar] [CrossRef]
  128. Ozkan, S.; Firatligil, F.B.; Sucu, S.; Dereli, M.L.; Kurt, D.; Yigit, A.; Reis, Y.A.; Yucel, K.Y.; Celen, S.; Engin-Ustun, Y. Can inflammatory biomarkers based on first trimester complete blood count parameters predict placental abruption?: A case-control study. J. Reprod. Immunol. 2024, 164, 104279. [Google Scholar] [CrossRef]
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Figure 1. The PRISMA flowchart for literature search and study selection.
Figure 1. The PRISMA flowchart for literature search and study selection.
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Jcm 15 02114 g002aJcm 15 02114 g002bJcm 15 02114 g002c
Figure 2. Forest plots showing the association between maternal C-reactive protein (CRP) levels and adverse neonatal outcomes [25,26,27,28,32,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109]. (A) Standardized Mean Difference (SMD) of maternal CRP levels in overall adverse neonatal outcomes compared with controls. (B) Odds of Preterm Birth in relation to elevated maternal CRP. (C) Odds of Low Birth Weight (LBW) in relation to elevated maternal CRP. (D) Odds of Small for Gestational Age (SGA) in relation to maternal CRP. (E) Odds of Stillbirth in relation to maternal CRP.
Figure 2. Forest plots showing the association between maternal C-reactive protein (CRP) levels and adverse neonatal outcomes [25,26,27,28,32,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109]. (A) Standardized Mean Difference (SMD) of maternal CRP levels in overall adverse neonatal outcomes compared with controls. (B) Odds of Preterm Birth in relation to elevated maternal CRP. (C) Odds of Low Birth Weight (LBW) in relation to elevated maternal CRP. (D) Odds of Small for Gestational Age (SGA) in relation to maternal CRP. (E) Odds of Stillbirth in relation to maternal CRP.
Jcm 15 02114 g002aJcm 15 02114 g002bJcm 15 02114 g002c
Table 1. Main Characteristics of included studies.
Table 1. Main Characteristics of included studies.
StudyCountrySample Size (N)Study DesignTrimester of CRP EvaluationCRP TypeAge of Participants
(years)		Neonatal Complications EvaluatedNewcastle–Ottawa Scale (NOS)
Assessment
CasesControls SelectionComparabilityOutcomeTotal
Ates et al., 2025 [25]Turkey411Prospective cohortMixCRP30.7 ± 5.429.1 ± 4.9PTB, IUGR★★★★★☆★★★8/9
Bakalis et al., 2012 [73]UK120Prospective cohort1sths-CRP33.0 (29.2–37.9) (median, IQR)32.9 (28.0–36.7) (median, IQR)PTB★★★★★☆★★★8/9
Biswas et al., 2025 [74]India180Prospective cohort3rd hs-CRP21.9 ± 4.321.9 ± 3.3LBW, PTB, IUGR★★★★★★★★★9/9
Bullen et al., 2012 [75]USA1310Prospective cohort2ndCRPNRNRPTB★★★★★★★★★9/9
Chen et al., 2023 [76]China618Prospective cohort1st hs-CRP33.7 32.6 PTB ★★★★★★★★★9/9
Cohen et al., 2014 [77]Israel89Prospective observational1st CRP 29.0 ± 7.632.2 ± 4.8IUGR★★★★☆☆★★★7/9
de Oliveira et al., 2017 [78]Brazil203 Prospective Cohort Mixhs-CRP20–40 SGA★★★★★☆★★★8/9
Deo et al., 2016 [79]India240 Case–ControlMixCRP25.3 ± 3.124.9 ± 3.3PTB★★☆★☆☆★★☆5/9
Erkenekli et al., 2014 [80]Turkey96 Case–Control3rd CRP26.3 ± 5.126.82 ± 5.27IUGR★★★★★☆★★★8/9
Ernst et al., 2011 [32]Netherlands6016Prospective Cohort1st hs-CRP29.8 ± 5.1PTB, LBW, SGA★★★★★★★★★9/9
Fischer-Suárez et al., 2016 [81]Spain123 Prospective Case–Control3rd hs-CRP27.2 ± 6.931.8 ± 4.0PTB★★★★☆☆★★☆6/9
Gahlot et al., 2016 [82]India112Prospective CohortMixCRP21–25PTB★★☆★☆☆★★☆5/9
Haedersdal et al., 2013 [83]Denmark218 Case–Control2nd CRP26.9 (18.2–37.3) (Median, range)27.5 (17.5–37.8) (Median, range)PTB, IUGR★★★★★☆★★★8/9
Halder et al., 2013 [84]India250 Prospective Cohort1st CRPNRNRPTB, LBW, IUGR★★★★☆☆★★☆6/9
Huang et al., 2020 [28]China618 Case–ControlMixCRP28.3 ± 5.328.2 ± 4.7PTB★★★★★★★★★9/9
Jyothi et al., 2023 [85]India100Prospective Cohort2nd CRP25.4 ± 4.026.4 ± 3.4PTB, LBW★★★★★☆★★★8/9
Karli et al., 2019 [86]Turkey53 Prospective Case–Control3rdCRP31 (27–34) (Median, IQR)28.5 (24.7–32) (Median, IQR)IUGR, LBW★★☆★★☆★★★7/9
Kennelly et al., 2022 [87]Ireland406 Prospective Cohort2nd CRP32.9 ± 4.632.2 ± 4.1PTB, LBW, SGA★★★★★★★★★9/9
Kirici et al., 2023 [88]Turkey100 Case–Control3rdhs-CRP28 (25–31) (Median, IQR)29 (25–32) (Median, IQR)IUGR, LBW★★☆★★☆★★★7/9
Kolelupun et al., 2022 [89]Indonesia48 Case–Control3rdhs-CRP27 (17–34) (Median, range)28 (17–34) (Median, range)PTB★★☆★☆☆★★☆5/9
Koleva-Korkelia et al., 2021 [90]Bulgaria220 Case–Control3rd CRP31.8 ± 4.126.9 ± 5.8PTB★★★★☆☆★★☆6/9
Lohsoonthorn et al., 2007 [91]USA1769 Prospective Cohort1stCRP32.2 ± 0.532.1 ± 0.1PTB★★★★★★★★★9/9
Madjid et al., 2020 [92]Indonesia80 Case–Control3rd CRPNRNRPTB★★☆★☆☆★★☆5/9
Mahapatra et al., 2021 [93]India156Prospective CohortMix CRP26.6 ± 3.9PTB, LBW★★★★★★★★★9/9
Mannava et al., 2016 [94]India210Prospective Cohort2ndCRP20–35PTB★★☆★☆☆★★☆5/9
Mansor & Farag, 2018 [95]Egypt500Prospective Cohort2nd hs-CRPNRNRPTB, LBW★★★★☆☆★★☆6/9
Moghaddam Banaem et al., 2012 [96]Iran778Prospective Cohort1st hs-CRP26 (23–28) (Median, 95% CI)26 (25–26) (Median, 95% CI)PTB★★★★★★★★★9/9
Nakishbandy & Barawi, 2014 [97]Iraq200 Case–Control2ndhs-CRP27.7 ± 5.928.9 ± 6.1PTB★★☆★☆☆★★☆5/9
Nikbakht et al., 2020 [26]Iran120Prospective Cohort1st CRP26.5 ± 4.4PTB, SGA★★★★★☆★★★8/9
Ozgu-Erdinc et al., 2014 [98]Turkey94 Prospective Cohort2ndhs-CRP35 (23–40) (Median, range)35 (19–44) (Median, range)PTB, IUGR★★★★☆☆★★☆6/9
Park et al., 2018 [27]South Korea173Retrospective Cohort3rdCRP33.0 ± 4.031.3 ± 4.0PTB★★★★★☆★★★8/9
Pitiphat et al., 2005 [99]USA234 Nested Case–Control1st hs-CRPNRNRPTB★★★★★★★★★9/9
Reron et al., 2004 [100]Poland389Retrospective Cohort3rdCRP27.5 ± 6.127.5 ± 6.1PTB★★☆★☆☆★★☆5/9
Rezaei et al., 2013 [101]Iran89Nested Cohort3rd CRP22.3 ± 3.722.7 ± 2.6PTB★★★★★☆★★★8/9
Rzepka et al., 2016 [102]Poland211Prospective Cohort3rdCRP30.2 ± 6.227.9 ± 5.9PTB★★★★★★★★★9/9
Savvidou et al., 2002 [103]UK90Cross-sectional2ndhs-CRP26.9 ± 6.029 ± 5.5IUGR★★★★★☆★★★8/9
Shahshahan et al., 2014 [104]Iran150 Prospective Cohort3rdCRP28.1 ± 4.627.1 ± 4.9PTB★★★★☆☆★★★7/9
Sharma et al., 2009 [105]India90 Prospective Cohort2ndCRP18–35PTB★★☆★☆☆★★☆5/9
Sinha et al., 2023 [106]India180Cross-sectional3rdCRP26.5 ± 4.126.1 ± 3.1PTB★★☆★☆☆★★☆5/9
Sorokin et al., 2010 [107]USA 475Prospective Cohort3rdCRPNRNRPTB★★★★★☆★★★8/9
Vijetha et al., 2022 [108]India359 Prospective Cohort2ndCRP20–30PTB, LBW, IUGR, Stillbirth★★☆★☆☆★☆☆4/9
ang et al., 2020 [109]China307Prospective Cohort2ndhs-CRP28.3 ± 3.928.3 ± 3.1LBW★★★★★☆★★★8/9
PTB: Preterm Birth, LBW: Low Birth Weight, IUGR: Intrauterine Growth Restriction, SGA: Small for Gestational Age, CRP: C-Reactive Protein, hs-CRP: High-Sensitivity C-Reactive Protein, NR: Not Reported.
Table 2. Subgroup analysis of included studies.
Table 2. Subgroup analysis of included studies.
VariableSub-Grouped byNo. of StudiesEffect Size (SMD)95% CII2 (%)p for
Heterogeneity
CRP levels and neonatal complicationsAgeYoung adult (20–30)200.740.28, 1.2197.400.000
Middle-aged adult (31–50)15−0.08−0.47, 0.3194.400.000
Neonatal ComplicationPreterm Birth (PTB)210.770.40, 1.1497.220.000
IUGR8−0.44−1.30, 0.4296.320.000
SGA3−0.05−0.46, 0.3637.710.200
LBW20.60−0.39, 1.5989.890.000
CRP typeCRP230.14−0.15, 0.4394.400.000
hs-CRP130.870.15, 1.5897.580.000
Pregnancy Trimester1st100.31−0.48, 1.0998.60.000
2nd130.23−0.12, 0.5991.00.000
3rd90.690.28, 1.1089.20.000
Mix20.430.00, 0.8730.80.230
VariableSub-Grouped byNo. of StudiesEffect Size (OR)95% CII2 (%)p for
Heterogeneity
Preterm birth (PTB)AgeYoung adult (20–30)183.942.55, 6.0787.20.000
Middle-aged adult (31–50)53.241.69, 6.2268.40.010
CRP TypeCRP152.721.98, 3.7367.30.000
hs-CRP86.842.69, 17.4493.10.000
CRP cut-off3.0–5.9 mg/L26.513.20, 13.250.00.700
6–8.9 mg/L154.712.89, 7.6686.10.000
≥9 mg/L61.831.19, 2.8166.00.010
Pregnancy Trimester1st62.411.49, 3.8981.60.000
2nd64.262.15, 8.4579.10.000
3rd116.142.88, 13.0986.70.000
Small for gestational age (SGA)AgeYoung adult (20–30)21.680.42, 6.6751.40.150
Middle-aged adult (31–50)31.170.52, 2.610.00.900
Low-birth weight (LBW)CRP TypeCRP42.391.08, 5.3084.80.000
hs-CRP32.430.80, 7.4088.50.000
CRP cut-off3.0–5.9 mg/L26.861.92, 24.5567.10.080
6–8.9 mg/L41.951.11, 3.4473.90.010
Pregnancy Trimester1st trimester21.330.76, 2.3571.00.060
2nd trimester42.891.06, 7.9486.00.000
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Mahereen, R.; Alsatli, A.; Albader, F.S.; Alqabbaa, R.I.; Abu Shehadeh, L.; Behairy, M.; Alaliw, G.; Alzahrani, L.T.; Alrafi, M.A.; Alganas, N.S.; et al. Association Between Maternal C-Reactive Protein (CRP) Levels and Adverse Neonatal Outcomes: A Systematic Review and Meta-Analysis. J. Clin. Med. 2026, 15, 2114. https://doi.org/10.3390/jcm15062114

AMA Style

Mahereen R, Alsatli A, Albader FS, Alqabbaa RI, Abu Shehadeh L, Behairy M, Alaliw G, Alzahrani LT, Alrafi MA, Alganas NS, et al. Association Between Maternal C-Reactive Protein (CRP) Levels and Adverse Neonatal Outcomes: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 2026; 15(6):2114. https://doi.org/10.3390/jcm15062114

Chicago/Turabian Style

Mahereen, Rutaba, Abdullah Alsatli, Faiza Said Albader, Rawan Ibrahim Alqabbaa, Lamar Abu Shehadeh, Mohamad Behairy, Ghezlan Alaliw, Lamees Tarek Alzahrani, Maria Abdulaziz Alrafi, Nojoud Sulaiman Alganas, and et al. 2026. "Association Between Maternal C-Reactive Protein (CRP) Levels and Adverse Neonatal Outcomes: A Systematic Review and Meta-Analysis" Journal of Clinical Medicine 15, no. 6: 2114. https://doi.org/10.3390/jcm15062114

APA Style

Mahereen, R., Alsatli, A., Albader, F. S., Alqabbaa, R. I., Abu Shehadeh, L., Behairy, M., Alaliw, G., Alzahrani, L. T., Alrafi, M. A., Alganas, N. S., Altaho, N. A., Baradwan, S., Mohamed, A. M., & Abu-Zaid, A. (2026). Association Between Maternal C-Reactive Protein (CRP) Levels and Adverse Neonatal Outcomes: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine, 15(6), 2114. https://doi.org/10.3390/jcm15062114

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