Skip to main content
Download PDF

Complement levels and their diagnostic utility in neonatal sepsis

BMC Pediatrics volume 25, Article number: 391 (2025) Cite this article

Abstract

Background

This study evaluated the diagnostic value of complement levels in neonatal sepsis and their roles in disease progression.

Methods

This diagnostic accuracy study, conducted in Guangdong Province, China (January 2021-February 2022), involved 41 neonates with sepsis and 41 controls matched by sex and gestational age. Cases included neonates with culture-positive or clinically diagnosed sepsis, while controls consisted of neonates hospitalized for non-septic conditions, confirmed by negative blood cultures. Ten complement components (C1q, C3, C3c, C3b, C4, C5, C5a, H, B, mannose-binding lectin [MBL]) were quantified using residual specimens from routine clinical tests. Descriptive statistics, logistic regression, ROC curve analysis, and correlation assessments were introduced in this study.

Results

The neonatal sepsis group had significantly higher levels of C3c (0.68 vs. 0.49 ng/mL, P = 0.007) and MBL (518.81 vs. 397.06 pg/mL, P < 0.001) compared to controls. In contrast, C5a levels were significantly lower in neonates with sepsis (51.18 vs. 57.25 ng/mL, P = 0.042). C5a demonstrated limited individual performance (AUC = 0.63, 95% CI: 0.51–0.75; sensitivity = 65.9%, specificity = 65.9% at 55.63 ng/mL), while MBL showed moderate accuracy (AUC = 0.75, 95% CI: 0.64–0.85; specificity = 90.2%, sensitivity = 53.7% at 512.86 pg/mL). Notably, their combined C5a + MBL indicator achieved exceptional discrimination (AUC = 0.92, 95% CI: 0.85–0.99) with 85.4% sensitivity and 97.6% specificity, yielding 97.2% positive predictive value (PPV) and 87.0% negative predictive value (NPV).Positive correlations were found between C4 levels and C-reactive protein (CRP), and C3 levels and neutrophil percentage (Neut%), while negative correlations were observed between C5 and MBL levels and total cholesterol (TCH).

Conclusions

This study highlights the diagnostic significance of combined C5a + MBL indicator in neonatal sepsis and underscores the association between complement levels and disease progression.

Peer Review reports

Background

Neonatal sepsis, a life-threatening systemic infection driven by immature immunity, causes significant global morbidity and mortality, affecting about 1.3 million infants annually with 17.6% mortality [1]. Severe complications like meningitis (23% of cases) exacerbate neurological and multi-organ damage [2, 3]. While blood culture remains the diagnostic gold standard, its limitations, including low sensitivity, high false positives, and delayed results, hinder timely intervention [4]. In recent years, biological factors, such as C-reactive protein (CRP), leukocytes, and procalcitonin (PCT), have been commonly used as diagnostic markers; however, these markers have low sensitivity and poor specificity [5].

Studies indicate the complement system’s crucial role in sepsis via diverse immune mechanisms, with complement levels closely correlated to sepsis patient conditions [6]. Key components like C1q and C3 correlate strongly with inflammation intensity and sepsis progression, offering diagnostic advantages over traditional markers. For instance, serum C1q levels aid in initial neonatal sepsis diagnosis and severity classification, as blood C1q levels are a good indicator of its severity [7]. Also, C3 and C5 levels in neonatal sepsis patients correlate positively with inflammation severity, suggesting their use as markers for sepsis onset and severity [8]. Other research highlights the associations between complement, lipid, and vitamin levels in sepsis development [9, 10]. In neonatal sepsis diagnosis, complement levels such as C3 and C1q offer advantages over common biomarkers like CRP and PCT. They can detect sepsis earlier, responding more rapidly to infection onset [11, 12]. Due to direct involvement in immune responses to bacterial infections, they show greater early-stage specificity [12], enabling more precise diagnosis and better early detection and treatment outcomes. However, few studies have explored the connection between various complements and biochemical indicators in neonatal sepsis patients.

This study addresses these gaps through a comprehensive evaluation of 10 complement components spanning classical, lectin, and alternative pathways in neonatal sepsis. We characterized complement level distributions, assessed their diagnostic accuracy for neonatal sepsis against conventional biomarkers. Furthermore, the associations among complement levels, inflammatory factors, lipids, and vitamin levels were explored to uncover holistic pathophysiological insights.

Methods

Study population

This diagnostic accuracy study was conducted between January 2021 and February 2022 at the Guangdong Women and Children Hospital, a large teaching tertiary public hospital with three hospital districts located in Guangzhou, Guangdong Province, China. The participants were enrolled in neonatal wards and intensive care units based on the 2019 Chinese Neonatal Sepsis Expert Consensus [13], the present study included both culture-positive and clinically proven neonatal sepsis: (1) Culture-positive Neonatal Sepsis: A neonate with clinical signs of sepsis (e.g., lethargy, hypothermia, hyperpyrexia, poor feeding, apnea, tachypnea, bradycardia, tachycardia, cyanosis, and poor perfusion) and a positive blood culture. (2) Clinically Proven Neonatal Sepsis: A neonate with negative blood culture but showing clinical signs of sepsis, along with at least one of the following: (1) ≥ 2 positive non-specific blood tests (e.g., leukocyte count, platelet count, C-reactive protein, procalcitonin), or (2) purulent meningitis changes in cerebrospinal fluid.

The inclusion criteria for the control group were: (1) neonates admitted during the same period; (2) matched by sex and gestational age; and (3) without clinical signs of sepsis, with negative blood cultures, and not meeting diagnostic criteria for neonatal sepsis. Blood cultures were obtained as part of routine clinical care, in accordance with national expert consensus [13–14], which recommends blood culture sampling in asymptomatic neonates with perinatal risk factors (e.g., maternal chorioamnionitis, intrapartum fever, premature rupture of membranes ≥ 18 h, or preterm birth). The exclusion criteria for this study were: (1) immune system abnormalities, including immunodeficiency diseases and autoimmune diseases; (2) severe neurological disorders, such as severe brain injury, hydrocephalus, or other neurological abnormalities (except for mild conditions such as meningocele); (3) genetic metabolic diseases or severe congenital abnormalities that could complicate the comparison or diagnosis of neonatal sepsis. (4) incomplete clinical medical records; (5) insufficient samples available for testing. Finally, a total of 41 neonates with sepsis and 41 controls were enrolled. Informed consent was obtained from the guardians of the enrolled children, and the study was approved by the Medical Ethics Committee of Guangdong Women and Children’s Hospital (No.202001102).

Sample collection and information extraction

Blood samples from the neonates were collected using sterile techniques by a trained healthcare professional, after obtaining informed consent from the parents or legal guardians. The blood samples were taken from the appropriate site depending on the neonate’s condition. The samples were drawn from residual blood collected during routine clinical tests, ensuring that no additional invasive procedures were performed. The collected blood was transferred into vacuum blood collection tubes, which were appropriately labeled. After collection, the samples were anonymized, centrifuged at 3000 r/min to separate the serum within 30 min, and immediately stored at − 80 ℃ for later analysis. Samples exhibiting visible hemolysis were excluded in our study. Basic information including sex, age, birth weight, and gestational age at birth, and medical records including admission diagnosis, discharge diagnosis, and category of infectious pathogens were obtained from the hospital’s medical record information system.

Complement and other biochemical tests

The sample was removed from the − 80 °C refrigerator and thawed to 25 °C at least 30 min. Quantitative analysis of complement in serum from children was performed following the manufacturer’s instructions and protocols. Immunoturbidimetric method was used to determine complement component levels including C3c, C1q, factor H and B, using detection kits (Shanghai Beijia Biochemistry Reagents Co., Ltd., China). OD values at a 340 nm wavelength were measured at a Beckman Coulter AU5800 automatic analyzer (Beckman Coulter, USA), and then were converted to the concentration values based on the standard curve. For C3, C4, C3b, C5a, C5, and mannose-binding lectin (MBL), enzyme-linked immunosorbent assay (ELISA) was applied, and serum samples were processed using reagents from Beijing Enzyme Immuno Bio-Technology Co (Beijing, China). The pretreated samples were detected in an automated enzyme immunoassay workstation (ADC ELISA 1800, Adikai Biotechnology Co., Ltd, China) at a wavelength of 450 nm. Two levels of quality control samples were conducted for each assay to monitor the precision and performance of the instrument following the calibration range. In addition, a blank sample was included in each experimental procedure to avoid potential contamination.

Inflammatory factors, including white blood cell count (WBC), neutrophils (Neut), neutrophil percentage (Neut%), and CRP, were measured using the Mindray BC-7500 hematology analyzer (Mindray Bio-Medical Electronics Co., Ltd., China). PCT levels were measured using the Roche cobas e411 electrochemiluminescence immunoassay analyzer (Roche Diagnostics, Germany). Triglycerides (TG), total cholesterol (TCH), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were measured using the Beckman AU5800 automated biochemical analyzer (Beckman Coulter, USA). Vitamin A and Vitamin E levels were measured using the Waters TQS micro liquid chromatography-tandem mass spectrometry system (Waters Corporation, USA).

Statistical analysis

Descriptive statistics were used to summarize the baseline characteristics of the study population. Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range), depending on their distribution. Categorical variables were expressed as counts and percentages. Group comparisons of complement levels were conducted using the Mann–Whitney U test. Logistic regression analysis was performed to examine the association between complement levels and neonatal sepsis risk. Model 1 was unadjusted, while Model 2 was adjusted for neonatal sex and age at sampling. Complement concentrations were standardized using Z-scores before inclusion in regression models, and odds ratios (ORs) were reported per 1-SD increase in complement level.

In accordance with the STARD (Standards for Reporting Diagnostic Accuracy Studies) 2015 guidelines [15], diagnostic performance was assessed for C5a, MBL, and their combination using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) was calculated along with 95% confidence intervals. Optimal cut-off values were determined using the Youden index. For each biomarker and the combined model, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), each with 95% confidence intervals derived using the Wilson score method. A joint diagnostic model combining C5a + MBL was developed using a logistic regression–based predictive probability approach. AUC values were compared among individual and combined models to evaluate improvement in diagnostic performance. To investigate the association between complement levels and progression of neonatal sepsis, the data was normalized and subjected to Pearson correlation analysis to correlate complement levels with lipid and inflammatory factor concentrations in children with neonatal sepsis. Statistical analysis was performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). A two-tailed P value < 0.05 was considered statistically significant.

Results

Basic characteristics of the study population

A total of 41 neonates with sepsis and 41 controls were included in this study. The basic characteristics of the study population and the distribution of types of infectious pathogens are shown in Table 1. The mean age of the study participants was 7.7 ± 8.4 days in the neonatal sepsis group and 4.0 ± 7.7 days for the control group, which was not significantly different (P = 0.772). Each group comprised 31 male subjects. The mean gestational age at birth for neonates with sepsis was 33.1 ± 6.7 weeks, with a mean birth weight of 2134.2 ± 935.5 g. In contrast, the mean gestational age at birth for neonates in the control group was 33.0 ± 3.1 weeks, with a mean birth weight of 2639.5 ± 837.1 g. The types of pathogenic bacteria and rates of infection with a particular type of pathogen among the study participants are listed in Table 1. A total of 23 patients in the neonatal sepsis group had pathogenic bacteria in their blood samples, whereas no bacteria were detected in the remaining 18 subjects. One of the patients was co-infected with Staphylococcus aureus and Klebsiella pneumoniae.

Table 1 Basic characteristics of the study population
Full size table

Distribution of ten complement levels in the neonatal sepsis group and the control group

The distribution of the ten complements in the neonatal sepsis and control groups is shown in Table 2. In the control group, factor H had the highest median level (397.50 ng/mL), while C5 had the lowest median level (13.27 pg/mL). The results demonstrated that C3c in the neonatal sepsis group were significantly higher than those in the control group (0.68 vs. 0.49 ng/mL, P = 0.007). In addition, neonatal sepsis subjects tended to have higher MBL levels than the control neonates (518.81 vs. 397.06 pg/mL, P < 0.001). In contrast, the median C5a concentration was 51.18 ng/ml in the neonatal sepsis group, which was significantly lower than the concentration in the control group (57.25 ng/ml), with the P = 0.042.

Table 2 Distributions of complement levels in neonatal sepsis and control groups
Full size table

Regression models of the ten complement levels in relation to neonatal sepsis

The measured complement data were Z-score-standardized to ensure the reliability and accuracy of the results, and the associations between complement levels and the odds of sepsis in neonates were analyzed using regression models (Table 3). The crude models used univariate regression models for each complement, whereas the adjusted models were adjusted for age and sex. The results of the crude model showed a significant association between complement C5a and MBL levels and the odds of neonatal sepsis (P < 0.05). These relationships remained statistically significant in adjusted models. For each 1-SD increase in complement C5a levels, the odds of neonatal sepsis was reduced by 53% (odds ratio [OR] = 0.47, 95% confidence interval [CI]: 0.24–0.90, P = 0.023). In contrast, for each 1-SD increase in complement MBL levels, the odds of neonatal sepsis increased by 2.96-fold (OR = 2.96, 95% CI: 1.61–5.44, P < 0.001).

Table 3 Associations between complement levels and the risk of neonatal sepsis
Full size table

Clinical diagnostic values of C5a, MBL, and C5a + MBL indicator in neonatal sepsis

Based on the results of regression models, C5a and MBL were selected as potential diagnostic biomarkers for neonatal sepsis, and their clinical diagnostic values were evaluated in accordance with STARD guidelines. As summarized in Table 4, C5a demonstrated an AUC of 0.63 (95% CI: 0.51–0.75, P = 0.042). Using a cut-off value of 55.63 ng/mL determined by the Youden index, C5a achieved a sensitivity of 65.9% (95% CI: 50.5–78.4%), specificity of 65.9% (95% CI: 50.5–78.4%), PPV of 65.9% (95% CI: 50.5–78.4%), and NPV of 65.9% (95% CI: 50.5–78.4%). In contrast, MBL showed improved diagnostic accuracy, with an AUC of 0.75 (95% CI: 0.64–0.85, P < 0.001). At a cut-off value of 512.86 pg/mL, the sensitivity was 53.7% (95% CI: 38.7–67.9%), specificity 90.2% (95% CI: 77.5–96.1%), PPV 84.6% (95% CI: 66.5–93.8%), and NPV 66.1% (95% CI: 53.0–77.1%). Notably, the combination of C5a + MBL, derived from a joint predictive probability model, achieved substantially better diagnostic performance. True positives, 4 false positives, 19 false negatives, and 37 true negatives. As shown in Fig. 1, the combined C5a + MBL indicator yielded an AUC of 0.92 (95% CI: 0.85–0.99, P < 0.001), with a sensitivity of 85.4% (95% CI: 71.6–93.1%), specificity of 97.6% (95% CI: 87.4–99.6%), PPV of 97.2% (95% CI: 85.8–99.5%), and NPV of 87.0% (95% CI: 74.3–93.9%).

Table 4 Diagnostic values of complements in neonatal sepsis
Full size table
Fig. 1
figure 1

ROC curve for the diagnostic values of C5a, MBL, and the combined C5a + MBL indicator in neonatal sepsis. Green line = C5a, blue line = MBL, and red line = C5a + MBL

Full size image

Correlation analysis of complement levels in relation to inflammatory factors, lipid, and vitamins

The levels of complement, inflammatory factors, lipid, and vitamins were logarithmically transformed, and the correlations were assessed by Pearson’s correlation coefficients (r) in the neonatal sepsis group. As shown in Fig. 2, there were moderate to high correlations between the ten complements, with Pearson’s r ranging from 0.34 (C1q and C3) to 0.90 (C3b and C5a). A strong correlation was observed between C3b, C5, C5a, and MBL (r > 0.70). C3c was not correlated with any of the other complementary components. The correlations between complement levels and inflammatory factors were assessed. Positive trends of C4 levels in relation to CRP (r = 0.33, P = 0. 047) and C3 levels in relation to Neut% (r = 0.34, P = 0.036) were suggested. Regarding the correlations between complement levels and lipid levels, TG exhibited a moderately positive correlation with complement C3, C4, factor H, and B (r ranging from 0.34 to 0.64). Increasing C5 (r = − 0.40, P = 0.014) and MBL (r = − 0.46, P = 0.004) levels could decrease TCH levels at a statistically levels. In addition, positive correlations of factor H levels in relation to HDL-C (r = 0.39, P = 0.015) and LDL-C levels (r = 0.62, P < 0.001), and factor B levels in relation to LDL-C levels (r = 0.63, P < 0.001) were observed. No significant correlation was suggested between the ten complements, vitamin A, and E (all P > 0.05).

Fig. 2
figure 2

Pearson correlation matrix of complement, inflammation factors, lipids, and vitamins among neonates with sepsis. The red color represented a positive correlation, while blue color represented a negative correlation. The darker the color, the greater the correlation coefficient. *P < 0.05, **P < 0.01, ***P < 0.001

Full size image

Discussion

This study investigated complement levels in neonates with sepsis and matched controls, revealing significant differences in C3c, C5a, and MBL. C5a levels were inversely associated with sepsis odds, while MBL showed a positive correlation. The combination of C5a + MBL demonstrated high diagnostic utility, suggesting their potential as biomarkers for neonatal sepsis.

The complement system, a key immune effector, comprises classical, lectin, and alternative pathways, mediating inflammation and pathogen clearance [16, 17]. Dysregulation of this system contributes to sepsis pathogenesis, making it a promising target for diagnostic exploration. In neonatal sepsis, we observed distinct activation patterns across complement components, with C3c (a C3 activation product) and MBL showing significant intergroup differences, while C5a exhibited paradoxical anti-inflammatory associations. C3c emerged as a dynamic marker of disease progression, with sensitivity superior to native C3. While prior studies linked low C3 levels to poor outcomes in septic meningitis [18], our findings suggest C3c reflects ongoing complement activation rather than static depletion. Conversely, C5a is a potent inflammatory mediator, which displayed a protective role, with higher levels reducing sepsis odds by 53% in our study. This aligns with mechanistic studies where C5a enhances leukocyte recruitment and activation. He et al. demonstrated that C5a plays a crucial role in activating the inflammatory response and that inhibiting its production effectively reduces inflammation in patients [19].

MBL plays a critical role in the inflammatory response and influences the regulation of inflammatory processes by triggering the complement cascade. MBL binds to pathogen-associated molecular patterns to activate the lectin complement pathway, facilitating pathogen clearance [20]. In our study, there was a significant disparity in the distribution of MBL between the two groups (median: 518.81 pg/ml for case vs. 397.06 pg/ml for control), and our results were consistent with those of Özkan et al. [21]. While MBL classically enhances pathogen clearance via complement activation, our study revealed elevated serum MBL levels in septic neonates. This may reflect a compensatory host response to acute infection, wherein MBL production is upregulated to neutralize pathogens [22]. However, excessive MBL could paradoxically exacerbate inflammation through uncontrolled complement activation, contributing to tissue damage and septic shock. The opposing roles of C5a and MBL suggest a complex interplay between pro- and anti-inflammatory complement mediators in neonatal sepsis. Excessive MBL activation may overstimulate the alternative complement pathway, leading to increased C3 cleavage and production of pro-inflammatory mediators (e.g., C3a). While C3a amplifies inflammation, C5a was downregulated as part of a feedback mechanism to limit excessive inflammation [23]. In our study, elevated MBL levels may drive complement activation beyond regulatory thresholds, suppressing C5a generation and limiting inflammation. This aligns with Neth et al.’s findings, where MBL enhances pathogen clearance but can also trigger excessive complement activation [24].

The C5a + MBL combination exhibits high diagnostic accuracy in this study, highlighting its potential as a supplementary tool for neonatal sepsis suspicion. Compared to the weak to moderate diagnostic value of C5a and MBL independently, the combined marker C5a + MBL (AUC = 0.92) has superior diagnostic accuracy to distinguish between septic and non-septic neonates. In addition, the sensitivity and specificity of C5a + MBL are significantly higher (85.4% and 97.6%, respectively), reducing the likelihood of both false negatives and false positives. The predictive values further support the superior performance of the combined marker. Furthermore, the PPV of 97.2% and the NPV of 87.0% indicated the reliability in accurately identifying both true-positive and true-negative neonatal sepsis cases. Among the current diagnostic markers for neonatal sepsis, microRNAs are emerging with high diagnostic potential. MiR-141 was shown by Jouza et al. to have a high diagnostic efficacy for neonatal sepsis, with an AUC of 0.87 [25]. In addition, a study by Fouda et al. also yielded high diagnostic efficacy for miR-15b and miR-378a (AUC 0.97, 0.95), with excellent diagnostic efficacy [26]. Elevated miR-15b levels suggest further exacerbation of neonatal sepsis. Conversely, a reduction in miR-378a levels in children with neonatal sepsis indicated a less severe condition. The diagnostic efficacy of these miRNA markers was comparable with that of the C5a + MBL combination used in this study. However, compared with the measurement of complement levels using serum samples, these markers were commonly complex, poorly localized, and time-consuming, which were significant limitations in early diagnosis for neonatal sepsis. Further prospective studies are warranted to validate the clinical applicability of C5a + MBL across diverse populations and integrate it with existing diagnostic protocols.

Lipid profiles, particularly TG, HDL-C, and LDL-C, showed significant alterations in sepsis, consistent with previous findings [10]. We observed a moderate positive correlation between C3, C4, factor H, and B with TG, HDL-C, and LDL-C levels, while C5 and MBL showed significant negative correlations with TCH, supporting the link between complement activation and metabolic dysregulation [27]. Regarding inflammation, elevated levels of inflammatory factors in the cerebrospinal fluid indicate potential inflammation, blood-brain barrier disruption, or CNS infection [28]. While no significant association was found between complement levels and PCT, WBC, or Neut%, we identified positive correlations between C4 and CRP, and between C3 and Neut%. Mechanistically, CRP activates the classical complement pathway by binding pathogens, triggering C4 cleavage [29], while C3 cleavage likely promotes neutrophil activation and inflammatory amplification, as C3 products enhance neutrophil responses and mediator release from macrophages/mast cells [30, 31].

This study measured the distribution characteristics of 10 complement components within three activation pathways and explored their diagnostic values in relation to neonatal sepsis. To the best of our knowledge, this is the first study to indicate a high diagnostic value of the combination of C5a + MBL indicator. However, several limitations warrant consideration. First, the limited sample size may restrict statistical power and generalizability. Second, pathogens were undetected in 43% of sepsis cases, likely due to prior antibiotic exposure, which could confound inflammatory marker levels. Third, combining early- and late-onset sepsis cohorts may obscure infection timing-specific biomarker variations. Finally, the control group did not explicitly include neonates clinically suspected but ruled out for sepsis, restricting applicability to ambiguous clinical scenarios. Future studies should validate our findings in larger cohorts, stratify analyses by infection onset timing, and prospectively incorporate clinically ambiguous subgroups.

Conclusion

In conclusion, this study demonstrates significant dysregulation of complement components in neonatal sepsis, characterized by elevated C3c and MBL levels coupled with reduced C5a concentrations compared to controls. The clinical diagnostic values of C5a, MBL, and their combined indicator had been explored. Individually, C5a and MBL biomarkers showed low-to-moderate diagnostic accuracy. The combined C5a + MBL indicator substantially enhanced diagnostic performance, achieving an excellent AUC along with high sensitivity and specificity. These findings advance our understanding of complement dysregulation in neonatal sepsis and provide novel insights into potential clinical applications. Identification of distinct complement signatures associated with sepsis could facilitate targeted diagnostic strategies. Future studies are warranted to validate these results in larger, prospective cohorts and to explore mechanistic pathways that participate in complement-mediated sepsis progression.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

AUC:

Area Under The Curve

CI:

Confidence Interval

CRP:

C-Reactive Protein

ELISA:

Enzyme-Linked Immunosorbent Assay

HDL-C:

High-Density Lipoprotein Cholesterol

LDL-C:

Low-Density Lipoprotein Cholesterol

MBL:

Mannose-Binding Lectin

NPV:

Negative Predictive Value

Neut:

Neutrophils

Neut%:

Neutrophil Percentage

OR:

Odds Ratio

PPV:

Positive Predictive Value

PCT:

Procalcitonin

ROC:

Receiver Operating Characteristic

SD:

Standard Deviation

TCH:

Total Cholesterol

TG:

Triglycerides

WBC:

White Blood Cell Count

References

  1. Fleischmann C, Reichert F, Cassini A, Horner R, Harder T, Markwart R, Tröndle M, Savova Y, Kissoon N, Schlattmann P, et al. Global incidence and mortality of neonatal sepsis: a systematic review and meta-analysis. Arch Dis Child. 2021;106(8):745–52.

    Article  PubMed  Google Scholar 

  2. Puopolo KM, Draper D, Wi S, Newman TB, Zupancic J, Lieberman E, Smith M, Escobar GJ. Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors. Pediatrics. 2011;128(5):e1155–1163.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kim KS. Acute bacterial meningitis in infants and children. Lancet Infect Dis. 2010;10(1):32–42.

    Article  CAS  PubMed  Google Scholar 

  4. He Y, Du WX, Jiang HY, Ai Q, Feng J, Liu Z, Yu JL. Multiplex cytokine profiling identifies Interleukin-27 as a novel biomarker for neonatal early onset Sepsis. Shock. 2017;47(2):140–7.

    Article  CAS  PubMed  Google Scholar 

  5. Marks L, de Waal K, Ferguson JK. Time to positive blood culture in early onset neonatal sepsis: A retrospective clinical study and review of the literature. J Paediatr Child Health. 2020;56(9):1371–5.

    Article  PubMed  Google Scholar 

  6. Skattum L, van Deuren M, van der Poll T, Truedsson L. Complement deficiency States and associated infections. Mol Immunol. 2011;48(14):1643–55.

    Article  CAS  PubMed  Google Scholar 

  7. Li H, Chen J, Hu Y, Cai X, Zhang P. Elevated serum C1q levels in children with Sepsis. Front Pead. 2021;9:619899.

    Article  Google Scholar 

  8. de Nooijer AH, Kotsaki A, Kranidioti E, Kox M, Pickkers P, Toonen EJM, Giamarellos-Bourboulis EJ, Netea MG. Complement activation in severely ill patients with sepsis: no relationship with inflammation and disease severity. Crit Care. 2023;27(1):63.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Zhang C, Fu S, Zhao M, Liu D, Zhao Y, Yao Y. Associations between complement components and vitamin D and the physical activities of daily living among a longevous population in Hainan, China. Front Immunol. 2020;11:1543.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Guo J, Lai W, Wu Y, Li H, Fu Z, Mu X. Change of serum lipoproteins and its potential use in stratifying patients with sepsis among neonates. Eur J Med Res. 2023;28(1):106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Schlapbach LJ, Mattmann M, Thiel S, Boillat C, Otth M, Nelle M, Wagner B, Jensenius JC, Aebi C. Differential role of the lectin pathway of complement activation in susceptibility to neonatal sepsis. Clin Infect Dis. 2010;51(2):153–62.

    Article  CAS  PubMed  Google Scholar 

  12. Charchaflieh J, Wei J, Labaze G, Hou YJ, Babarsh B, Stutz H, Lee H, Worah S, Zhang MJJIR. The role of complement system in septic shock. J Inflamm Res. 2012;2012(1):407324.

    Google Scholar 

  13. Subspecialty Group of Neonatology, the Society of Pediatric, Chinese Medical Association. Professional committee of infectious diseases, neonatology society, Chinese medical Doctor association. Expert consensus on the diagnosis and management of neonatal sepsis (version 2019) [Article in Chinese]. Chin J Pediatr. 2019;57(4):252–7.

    Google Scholar 

  14. Pediatric Disease Laboratory Medicine Expert Committee of Laboratory Medicine Branch of Chinese Medical Doctor Association. Chinese experts’ consensus on standardized specimen collection for children’s blood culture [Article in Chinese]. Chin J Lab Med. 2020;43(5):547–52.

    Google Scholar 

  15. Cohen JF, Korevaar DA, Altman DG, Bruns DE, Gatsonis CA, Hooft L, Irwig L, Levine D, Reitsma JB, de Vet HC, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11):e012799.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Barichello T, Generoso JS, Singer M, Dal-Pizzol F. Biomarkers for sepsis: more than just fever and leukocytosis-a narrative review. Crit Care. 2022;26(1):14.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Abe T, Kubo K, Izumoto S, Shimazu S, Goan A, Tanaka T, Koroki T, Saito K, Kawana R, Ochiai H. Complement activation in human Sepsis is related to Sepsis-Induced disseminated intravascular coagulation. Shock. 2020;54(2):198–204.

    Article  CAS  PubMed  Google Scholar 

  18. Goonetilleke UR, Scarborough M, Ward SA, Hussain S, Kadioglu A, Gordon SB. Death is associated with complement C3 depletion in cerebrospinal fluid of patients with Pneumococcal meningitis. mBio 2012, 3(2).

  19. Hellerud BC, Orrem HL, Dybwik K, Pischke SE, Baratt-Due A, Castellheim A, Fure H, Bergseth G, Christiansen D, Nunn MA, et al. Combined Inhibition of C5 and CD14 efficiently attenuated the inflammatory response in a Porcine model of meningococcal sepsis. J Intensive Care. 2017;5:21.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Turner MW. The role of mannose-binding lectin in health and disease. Mol Immunol. 2003;40(7):423–9.

    Article  CAS  PubMed  Google Scholar 

  21. Özkan H, Köksal N, Çetinkaya M, Kiliç Ş, Çelebi S, Oral B, Budak F. Serum mannose-binding lectin (MBL) gene polymorphism and low MBL levels are associated with neonatal sepsis and pneumonia. J Perinatol. 2012;32(3):210–7.

    Article  PubMed  Google Scholar 

  22. Dean M, Minchinton R, Heatley S, Eisen DJJCI. Mannose binding lectin acute phase activity in patients with severe infection. J Clin Immunol. 2005;25:346–52.

    Article  CAS  PubMed  Google Scholar 

  23. Yan C, Gao H. New insights for C5a and C5a receptors in sepsis. Front Immunol. 2012;3:368.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Neth O, Jack DL, Dodds AW, Holzel H, Klein NJ, Turner MW. Mannose-binding lectin binds to a range of clinically relevant microorganisms and promotes complement deposition. Infect Immun. 2000;68(2):688–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Jouza M, Bohosova J, Stanikova A, Pecl J, Slaby O, Jabandziev P. MicroRNA as an early biomarker of neonatal Sepsis. Front Pediatr. 2022;10:854324.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Fouda E, Elrazek Midan DA, Ellaban R, El-Kousy S, Arafat E. The diagnostic and prognostic role of MiRNA 15b and MiRNA 378a in neonatal sepsis. Biochem Biophys Rep. 2021;26:100988.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Mecatti GC, Messias MCF, de Oliveira Carvalho P. Lipidomic profile and candidate biomarkers in septic patients. Lipids Health Dis. 2020;19(1):68.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Vasunilashorn SM, Ngo LH, Dillon ST, Fong TG, Carlyle BC, Kivisäkk P, Trombetta BA, Vlassakov KV, Kunze LJ, Arnold SE, et al. Plasma and cerebrospinal fluid inflammation and the blood-brain barrier in older surgical patients: the role of inflammation after surgery for elders (RISE) study. J Neuroinflammation. 2021;18(1):103.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhu H, Lu B, Shi C, Xie H, Ren Y, Feng J. Detection of several serum factors in neonatal gonococcal conjunctivitis and its clinical significance. Biomed Res. 2017;28(17):7467–72.

    CAS  Google Scholar 

  30. Lendak D, Mihajlovic D, Mitic G, Ubavic M, Novakov-Mikic A, Boban J, Brkic S. Complement component consumption in sepsis correlates better with hemostatic system parameters than with inflammatory biomarkers. Thromb Res. 2018;170:126–32.

    Article  CAS  PubMed  Google Scholar 

  31. Flierl MA, Rittirsch D, Nadeau BA, Day DE, Zetoune FS, Sarma JV, Huber-Lang MS, Ward PA. Functions of the complement components C3 and C5 during sepsis. FASEB J. 2008;22(10):3483–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors wanted to thank participators who volunteered to participate in this study.

Funding

This research was funded by the National Natural Science Foundation of China (Grant No.42207492) and the Natural Science Foundation of Guangdong Province of China (Grant No.2022A1515012226).

Author information

Authors and Affiliations

  1. Department of Clinical Laboratory, Guangdong Women and Children Hospital, Guangzhou, 511443, China

    Fuqiang Diao, Feng Zhong, Lingling Tang, Chunming Gu, Junfei Guo, Lihong Wu, Weixiang Wu & Mingyong Luo

Authors
  1. Fuqiang Diao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Feng Zhong
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lingling Tang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chunming Gu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Junfei Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Lihong Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Weixiang Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Mingyong Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Fuqiang Diao: Conceptualization, Methodology, Formal analysis, Visualization, Writing-original draft. Feng Zhong: Conceptualization, Data curation, Methodology, Formal analysis, Software. Lingling Tang: Methodology, Formal analysis, Visualization, Writing-review & editing. Chunming Gu: Investigation, Methodology. Junfei Guo: Methodology, Formal analysis. Lihong Wu: Investigation, Validation. Weixiang Wu: Conceptualization, Methodology, Formal analysis, Funding acquisition, Visualization, Writing-original draft. Mingyong Luo: Funding acquisition, Project administration, Supervision, Resources, Writing-review & editing.

Corresponding authors

Correspondence to Weixiang Wu or Mingyong Luo.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Medical Ethical Committee of Guangdong Women and Children Hospital (No.202001102). All healthcare procedures were conducted in accordance with approved guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diao, F., Zhong, F., Tang, L. et al. Complement levels and their diagnostic utility in neonatal sepsis. BMC Pediatr 25, 391 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12887-025-05729-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12887-025-05729-y

Keywords

BMC Pediatrics

ISSN: 1471-2431

Contact us