Fibulin2 is a good diagnostic and prognostic indicator for sepsis

Abstract

Background

Sepsis is a life-threatening syndrome characterized by multiple organ dysfunction resulting from a maladjusted host response to infection. It remains a leading cause of morbidity and mortality worldwide. Early intervention for sepsis improves clinical outcomes. This study aimed to develop a diagnostic and prognostic indicator at the onset of sepsis.

Methods

From January 2021 to December 2023, patients in the emergency department (ED) were evaluated for the use of Fibulin2 as a diagnostic and prognostic indicator for sepsis. The levels of Fibulin2 in plasma were detected via enzyme-linked immunosorbent assay (ELISA). Receiver operating characteristic (ROC) curves were generated to assess the performance of Fibulin2 for predicting sepsis.

Results

The level of serum Fibulin2 was significantly higher in patients with sepsis than in controls, and Fibulin2 had diagnostic value for sepsis and performed better than C-reactive protein (CRP), procalcitonin (PCT), the white blood cell count (WBC), the neutrophil ratio (NEU%), and D-dimer. Overall, Fibulin2 was upregulated in patients with septic shock compared with septic patients without shock, and Fibulin2 can be used to diagnose septic shock. Moreover, Fibulin2 was increased in patients who died at 28 days of sepsis and had the potential to predict 28-day mortality.

Conclusion

Fibulin2 may serve as a diagnostic and prognostic biomarker for sepsis in the ED.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-025-11282-x.

Introduction

Sepsis is considered a life-threatening syndrome characterized by multiple organ dysfunction because of a disordered host response to infection [1, 2]. With advances in medicine, numerous studies have focused on the molecular mechanism, clinical diagnosis and prognosis of sepsis. However, it remains a leading cause of morbidity and mortality worldwide [3]. More than 49 million individuals are diagnosed with sepsis yearly, and approximately 11 million people die of sepsis, accounting for approximately 19.7% of all deaths worldwide [4, 5]. Sepsis imposes appreciable encumbrance on our society; as a result, we should make efforts to address it vigorously [6, 7].

The usual development process of sepsis involves sepsis developing into septic shock and ultimately into death. According to previous studies, once sepsis becomes severe, mortality increases sharply from approximately 20% to more than 40% when sepsis with intrinsic organ dysfunction progresses to septic shock characterized by refractory hypotension [8]. Therefore, personalizing interventions for “high-risk” sepsis at an early stage is critical for better clinical outcomes. Accordingly, the key to sepsis intervention is improving the ability to quickly diagnose and stratify patients according to risk, which often relies on ED [9, 10]. At present, the diagnosis and risk stratification of sepsis are based on the isolation of microbes, biochemical methods and molecular techniques. Although microbiologic culture is recognized as the gold standard to diagnosis sepsis, it is time-consuming and costs 48–72 h, which may result in delays in diagnosis and treatment. Moreover, only 5–10% of blood cultures contain microorganisms, and the insufficient sensitivity may not support the detection of sepsis enough [11]. Laboratory tests of biomarkers, including C-reactive protein (CRP), the neutrophil ratio (NEU%), and procalcitonin (PCT), are routine biochemical methods. The sensitivity and specificity of these methods are currently inadequate for the early diagnosis and risk stratification of sepsis [12]. Moreover, molecular approaches may not be affordable for many medical institutions, as they require expensive technologies and equipment [11]. Thus, improved biomarkers to diagnose and predict the prognosis of sepsis patients in the emergency department are still needed [13].

Fibulin2 is a secreted calcium-binding extracellular matrix glycoprotein widely expressed in many tissues, such as plasma, bone, and skin [14]. Importantly, Fibulin2 can be detected in plasma via enzyme-linked immunosorbent assay (ELISA). Our preliminary study reported that Fibulin2 is upregulated in the plasma during infection and is a promising diagnostic biomarker for predicting early infection [11]. Because sepsis is resulted from uncontrolled infection, it is reasonable to speculate that Fibulin2 may also be a potential new biomarker to diagnose sepsis and predict its outcome. In this study, we will reveal that whether Fibulin2 may behave as a biomarker for the early diagnosis of sepsis, Fibulin2 might be used to predict the severity of sepsis in the emergency department, and Fibulin2 may be inferior to CRP, PCT, or other common biomarkers.

Methods

Study design and setting

This was a single-center clinical diagnostic accuracy study executed at the ED of the Army Medical Center of the Chinese People’s Liberation Army, which is a 60-bed tertiary care department with an annual load of approximately 100,000 ED outpatients and more than 800 ED inpatients. This study complied with the provisions outlined in the Declaration of Helsinki and was approved by the Clinical Ethics Committee of the Army Medical Center of the Chinese People’s Liberation Army [Approval number: Medical Research Review (2021) NO 07 and Medical Research Review (2024) NO 295]. From June 2021 to December 2023, consecutive patients who agreed to participate in this study were recruited, including patients with sepsis and nonseptic control patients admitted to the ED of our hospital. All participants or their legal representatives signed a written informed consent form before participating in this study.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) patients who were admitted to the ED of the Army Medical Center of the Chinese People’s Liberation Army with a clinical diagnosis of sepsis, which was determined by the initial clinicians of the ED after their clinical data were carefully estimated and who were independently blinded to the study results; (2) patients without sepsis who volunteered to participate in this study and were willing to provide blood samples within 12 h of admission. The exclusion criteria were as follows: (1) patients with a tumor or who had experienced a stroke or acute myocardial infarction; (2) patients less than 18 years old; (3) incomplete medical records; (4) ambiguous diagnosis; (5) pregnancy; (6) patients who were admitted to the ED because of trauma. All participants were divided into a sepsis group and a control group (nonsepsis group) based on their diagnosis at admission.

Determination of sepsis and septic shock

According to the Sepsis-3 guidelines, sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Briefly, patients who had a sequential organ failure assessment (SOFA) score ≥ 2 due to current infection were clinically defined as having sepsis. The quick SOFA (qSOFA) score was also used as a prompt method to identify patients with suspected infection who were at greater risk of a poor outcome in the ED. The qSOFA score ranges from 0 to 3 using three indicators: low blood pressure (systolic blood pressure ≤ 100 mmHg), high respiratory rate (≥ 22 breaths/min), or altered mentation (Glasgow Coma Scale score < 15), and each was assigned one point. Patients who presented with 2 or more qSOFA signs at the onset of infection were also diagnosed with sepsis. Septic shock is a severe subset of sepsis in which patients are identified to take vasopressor medicine to maintain a mean arterial pressure ≥ 65 mmHg and a serum lactate level ≥ 2 mmol/L in the absence of hypovolemia. Those who did not meet the criteria for sepsis during their ED stay constituted the control group, whereas patients who met them were assigned to the sepsis group or septic shock group [15].

Following data collection, at least two septic disease specialists and one emergency attending physician examined the medical records, reviewed the clinical data of the participants to diagnose sepsis and then classified them into the sepsis, sepsis, and septic shock groups according to the Sepsis-3 definitions.

Data collection

In addition to the level of Fibulin2 in plasma, relevant clinical data were collected from the medical records of our hospital, including but not limited to age, sex, chief complaint, disease history, comorbidities, vital signs, presence of infection, SOFA score, qSOFA score and corresponding laboratory values, such as the levels of proalbumin, albumin, alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, bilirubin, WBC, NEU%, platelets, D-dimer, creatinine, glomerular filtration, CRP, PCT, and IL-6 and the erythrocyte sedimentation rate. The above data were obtained by an experienced collector via standardized data collection forms and recorded via Excel 2016 (Microsoft Corporation, Redmond, Washington). All relevant data were examined by another trained researcher to assess the validity of the data collection, and any inconsistencies were corrected.

Detection of Fibulin2

Venous blood samples from consecutive patients enrolled in this study were obtained in the ED within 12 h after admission. These samples were collected in tubes containing heparin, centrifuged at 4000 × g for 5 min to obtain plasma, and stored at −80 °C until use. The level of Fibulin2 was measured within 7 days after collection via an Enzyme-linked Immunosorbent Assay Kit for Human Fibulin2 (Cloud-Clone Corp, Wuhan, China), with a normal reference range of 0.78–50 ng/mL. The sensitivity: minimum detectable dose < 5.4pg/mL. The precision: intra-assay precision with CV < 10% and inter-assay precision with CV < 12%. All the measurements were repeated twice, and the average for each sample was taken. The operators were unaware of the related clinical information [16].

Follow-up analysis

When patients were admitted, the treatment plan was formulated by senior doctors based on the patient’s condition. For discharged patients, follow-up was typically carried out by telephone, whereas for hospitalized patients, their clinical information was acquired via the clinical records system at any time. A 28-day follow-up survey [17] recorded the deaths of patients with sepsis after ED admission, including both in-hospital and posthospital mortality within that timeframe.

Statistical analysis

We performed statistical analysis via MedCalc version 20.022 (MedCalc Software Ltd., Ostend, Belgium) and SPSS Statistics version 25 (SPSS Inc., Chicago, IL, USA). GraphPad Prism version 8 (GraphPad Software Inc., La Jolla, CA, USA) was used to construct the figures. All quantitative variables are presented as the means ± SDs; qualitative variables are presented as frequencies (percentages). Differences between quantitative variables were compared using analysis of Student’s t-test or Mann–Whitney U test. Percentages were compared with the chi-square test or Fisher’s exact test. ROC curves were calculated to measure the sensitivity and specificity of biomarkers to diagnose sepsis, septic shock, or nonsurviving patients with sepsis on Day 28. In this method, a perfect biomarker has 100% sensitivity, shows no false-positives (100% specificity), and produces an area under the curve (AUC) of 1.0, whereas a biomarker with no diagnostic value has an AUC of no more than 0.5. Youden’s index, with the highest sum of sensitivity and specificity, was used to determine the optimal cutoff value for differentiation. The Z test was used to compare the differences in ROC curves among various biomarkers. Logistical regression and linear regression were used to assess the association between different biomarkers and infection. A probability of p < 0.05 was considered the threshold of significance.

Results

Characteristics of the participants

The flow chart of the study population is depicted in Fig. 1. A total of 796 patients were screened from June 2021 to December 2023. A total of 334 patients were excluded because 318 did not meet the inclusion criteria, and 16 patients dropped out of the study halfway. A total of 462 individuals who were divided into a control group containing 227 nonseptic patients and a sepsis group containing 235 septic patients, depending on the clinical diagnosis, were subsequently included in the study. Then, according to the risk classification, 235 septic patients were split into 83 patients with septic shock and 152 patients without septic shock. In parallel, in accordance with the 28-day mortality results, 235 septic patients were divided into a survival group with 180 individuals and a nonsurvival group with 55 individuals.

Fig. 1.

Flowchart of the study population. The data are presented as the means ± SDs. ROC curve: receiver operating characteristic curve

As shown in Table 1, the mean age was 58.33 ± 15.34 years in the control group and 60.85 ± 21.08 years in the sepsis group. Age, sex, medical history and comorbidities were not significantly different between the two groups (P > 0.05). The mean weight in the sepsis group was 58.69 ± 11.59 kg/m², lower than that in the control group, which was 65.64 ± 11.81 kg/m² (t = 4.43, P = 0.00). Prealbumin, albumin, WBC, NEU%, D-dimer, CRP, PCT, IL-6, and Fibulin2 levels were significantly greater in the sepsis group than in the control group (P < 0.05).

Table 1.

Baseline characteristics of all the participants

	Control	Sepsis	Statistical value
n	227	235
Age (years)	58.33 ± 15.34	60.85 ± 21.08	t=−1.47, P = 0.14
Gender, male (female) (n)	135(92)	133(102)	χ2 = 0.39, P = 0.53
Weight (kg)	65.64 ± 11.81	58.69 ± 11.59	t = 4.43, P = 0.00
Medical history (n)(%)
Smoking	35(15.4%)	44(18.7%)	χ2 = 0.89, P = 0.35
Alcohol consumption	25(11.0%)	35(14.9%)	χ2 = 1.54, P = 0.22
Comorbidities (n)(%)
Diabetes mellitus	29(12.8%)	33(14.0%)	χ2 = 0.16, P = 0.69
Hypertension	48(21.1%)	64(27.2%)	χ2 = 2.33, P = 0.13
Coronary heart disease	16(7.0%)	27(11.5%)	χ2 = 2.70, P = 0.10
Vital signs and mental status at time of admission
Respiratory rate (breaths/min)	19.71 ± 1.51	19.84 ± 2.04	t=−0.56, P = 0.57
Pulse rate (times/min)	84.59 ± 16.60	89.98 ± 17.97	t=−2.61, P = 0.01
Mean arterial pressure (mmHg)	96.03 ± 13.36	94.19 ± 17.45	t = 1.05, P = 0.29
Disturbance of consciousness n (%)	3(1.3%)	18(7.7%)	χ2 = 10.69, P = 0.00
Laboratory values, mean ± SD
Prealbumin (mg/L)	195.37 ± 100.93	144.93 ± 87.40	t = 3.12, P = 0.00
Albumin (g/L)	40.09 ± 6.12	37.00 ± 7.27	t = 4.27, P = 0.00
Alanine aminotransferase (U/L)	46.38 ± 64.14	48.93 ± 90.35	t=−0.29, P = 0.77
Aspartate aminotransferase (U/L)	38.68 ± 54.51	64.51 ± 330.33	t=−0.94, P = 0.35
Bilirubin (µmol/L)	15.64 ± 14.62	16.66 ± 14.42	t=−0.59, P = 0.55
White blood cells (109/L)	7.88 ± 3.26	9.70 ± 4.61	t=−4.77, P = 0.00
NEU%	70.85 ± 11.64	76.30 ± 13.27	t=−4.41, P = 0.00
Platelets (109/L)	227.84 ± 85.54	211.47 ± 94.67	t = 1.87, P = 0.06
D-dimer (ng/mL)	313.02 ± 523.02	1169.75 ± 2946.47	t=−3.67, P = 0.00
Creatinine (mg/dL)	111.02 ± 160.57	110.32 ± 161.29	t = 0.04, P = 0.97
Glomerular filtration (mL/min/1.73 m2)	116.52 ± 44.47	115.72 ± 58.95	t = 0.14, P = 0.89
C-reactive protein (mg/L)	11.48 ± 25.20	37.03 ± 53.20	t=−6.35, P = 0.00
Procalcitonin (ng/mL)	0.44 ± 1.78	2.73 ± 12.75	t=−2.02, P = 0.05
Interleukin-6 (pg/mL)	62.24 ± 179.47	275.81 ± 928.58	t=−2.18, P = 0.03
Erythrocyte sedimentation rate (mm/h)	29.00 ± 22.96	42.19 ± 32.90	t=−1.39, P = 0.17
Fibulin2 (ng/mL)	4.33 ± 1.99	6.91 ± 2.55	t=−12.17, P = 0.00

Next, to explore the predictive value of different biomarkers for the risk classification of sepsis, all septic patients were divided into a septic shock group and a sepsis without shock group. As presented in Supplementary Table 1, there were no significant differences in age, sex, weight, or medical history between the two groups (P > 0.05). The levels of bilirubin, creatinine, PCT, and Fibulin2 were significantly greater in the septic shock group than in the sepsis without shock group (P < 0.05). The levels of platelets and glomerular filtration were significantly lower in the septic shock group than in the sepsis without shock group (P < 0.05).

Similarly, all septic patients were divided into a survival group and a nonsurvival group according to survival status. As presented in Supplementary Table 2, there were no significant differences in age or weight between the two groups (P > 0.05). The levels of bilirubin, creatinine, and Fibulin2 were significantly greater in the nonsurviving group than in the surviving group (P < 0.05). The levels of glomerular filtration were significantly lower in the nonsurviving group than in the surviving group (P < 0.05).

Comparison of the performance of Fibulin2 with other biomarkers for the diagnosis of sepsis in all patients

The level of Fibulin2 in patients with sepsis (6.91 ± 2.55 ng/mL) was significantly greater than that in patients without sepsis (4.33 ± 1.99 ng/mL) (t=−12.17, P = 0.00) (Fig. 2a). The ROC curves of Fibulin2 (AUC 0.82, P = 0.00, 95% CI 0.78–0.86), CRP (AUC 0.71, P = 0.00, 95% CI 0.66–0.76), PCT (AUC 0.65, P = 0.00, 95% CI 0.56–0.74), WBC (AUC 0.64, P = 0.00, 95% CI 0.59–0.70), NEU% (AUC 0.65, P = 0.00, 95% CI 0.60–0.71), IL-6 (AUC 0.61, P = 0.05, 95% CI 0.51–0.72), and D-dimer (AUC 0.70, P = 0.03, 95% CI 0.64–0.75) for the diagnosis of sepsis are presented in Fig. 2b, c, d, e, f, g, and h, respectively.

Fig. 2.

Performance of Fibulin2 in the diagnosis of sepsis among different biomarkers. a Levels of Fibulin2 in plasma from patients with or without infection. b, c, d, e, f, g, h Receiver operating characteristic curves (ROC curve) of Fibulin2, CRP, PCT, WBC, NEU%, IL-6, and D-dimer to diagnose sepsis. Fibulin2 (AUC 0.82, P = 0.00, 95% Cl 0.78–0.86), CRP (AUC 0.71, P = 0.00, 95% Cl 0.66–0.76), PCT (AUC 0.65, P = 0.00, 95% Cl 0.56–0.74), WBC (AUC 0.64, P = 0.00, 95% Cl 0.59–0.70), NEU% (AUC 0.65, P = 0.00, 95% Cl 0.60–0.71), IL-6 (AUC 0.61, P = 0.05, 95% Cl 0.51–0.72), and D-dimer (AUC 0.70, P = 0.03, 95% Cl 0.64–0.75)

In summary, Fibulin2, CRP, PCT, WBC, NEU% and D-dimer can be used to predict sepsis. However, the ROC curve analysis revealed that the predictive efficiency of IL-6 was insufficient.

By comparing the AUC values, we found that the diagnostic ability of Fibulin2 was better than that of CRP (Z = 3.48, P = 0.00), PCT (Z = 2.93, P = 0.00), WBC (Z = 5.05, P = 0.00), NEU% (Z = 4.11, P = 0.00), and D-dimer (Z = 2.58, P = 0.01). The cutoff values of these biomarkers and other indicators for evaluating diagnostic efficacy are presented in Table 2; each had the highest odds ratio value. Generally, when the cutoff value was 5.25 for Fibulin2, the Youden index had a maximum value of 0.52, the sensitivity was 73.19%, and the specificity was 78.85%.

Table 2.

Performance of different biomarkers for predicting sepsis

Biomarkers	Cut off value	Sensitivity	Specificity	Youden Index	PPV	NPV	PLR	NLR	Odds ratio
Fibulin2	5.25	73.19%	78.85%	0.52	77.59%	74.63%	3.46	0.34	10.18
CRP	8.28	58.85%	74.57%	0.33	69.82%	64.44%	2.31	0.55	4.19
PCT	0.10	59.70%	69.05%	0.29	65.86%	63.15%	1.93	0.58	3.31
WBC	9.65	48.28%	81.22%	0.30	71.99%	61.09%	2.57	0.64	4.04
NEU%	78.05	55.80%	72.60%	0.28	67.07%	62.16%	2.04	0.61	3.35
IL-6	28.59	47.47%	71.43%	0.19	62.43%	57.63%	1.66	0.74	2.26
D-Dimer	236.47	61.80%	71.50%	0.33	68.44%	65.18%	2.17	0.53	4.06

PPV Positive predictive value, NPV Negative predictive value, PLR Positive likelihood ratio, NLR Negative likelihood ratio

Comparison of the performance of Fibulin2 with that of other biomarkers for the prediction of septic shock in patients with sepsis

The level of Fibulin2 in patients with septic shock (7.65 ± 2.65 ng/mL) was significantly greater than that in septic patients without shock (6.52 ± 2.41) (t=−3.31, P = 0.00) (Fig. 3a). The ROC curves of Fibulin2 (AUC 0.63, P = 0.00, 95% CI 0.56–0.71), CRP (AUC 0.56, P = 0.13, 95% CI 0.48–0.64), PCT (AUC 0.68, P = 0.00, 95% CI 0.58–0.77), WBC (AUC 0.46, P = 0.29, 95% CI 0.38–0.54), NEU% (AUC 0.60, P = 0.01, 95% CI 0.52–0.69), IL-6 (AUC 0.59, P = 0.12, 95% CI 0.47–0.71), and D-dimer (AUC 0.53, P = 0.49, 95% CI 0.44–0.63) for the diagnosis of sepsis are presented in Fig. 3b, c, d, e, f, g, h, respectively.

Fig. 3.

Performance of Fibulin2 in the prediction of septic shock in patients with sepsis among different biomarkers. a Levels of Fibulin2 in plasma from patients with or without shock. b, c, d, e, f, g, h Receiver operating characteristic curves (ROC curve) of Fibulin2, CRP, PCT, WBC, NEU%, IL-6, and D-dimer to predict septic shock. Fibulin2 (AUC 0.63, P = 0.00, 95% Cl 0.56–0.71), CRP (AUC 0.56, P = 0.13, 95% Cl 0.48–0.64), PCT (AUC 0.68, P = 0.00, 95% Cl 0.58–0.77), WBC (AUC 0.46, P = 0.29, 95% Cl 0.38–0.54), NEU% (AUC 0.60, P = 0.01, 95% Cl 0.52–0.69), IL-6 (AUC 0.59, P = 0.12, 95% Cl 0.47–0.71), and D-dimer (AUC 0.53, P = 0.49, 95% Cl 0.44–0.63)

Fibulin2, PCT and the NEU% were biomarkers for the auxiliary diagnosis of septic shock in patients with sepsis. However, CRP, WBC, IL-6, and D-dimer may have no diagnostic capacity for predicting septic shock in patients with shock. A comparison of the AUC values revealed that the diagnostic ability of Fibulin2 was not worse than that of PCT (Z = 0.40, P = 0.69) or NEU% (Z = 0.12, P = 0.91). The cutoff values and other indicators for evaluating the diagnostic efficacy of Fibulin2, PCT and NEU% are presented in Supplementary Table 3, each of which had the highest odds ratio value. Generally, when the cutoff value was 6.43 for Fibulin2, the Youden index had a maximum value of 0.23, the sensitivity was 68.67%, and the specificity was 54.61%. Logistical regression and linear regression were also applied to assess the association between different biomarkers and sepsis and related data was shown in Supplementary Tables 4 and Supplementary Table 5.

Comparison of the performance of Fibulin2 with other biomarkers for survival status on Day 28 in patients with sepsis

The 28-day period is important for patients with sepsis, as it is a critical window for patient outcomes and can reflect the acute phase of the disease. It assists medical care providers in determining medical decisions during this crucial timeframe [18]. Therefore, 28-day mortality was selected as the follow-up index. The level of Fibulin2 in septic patients who died at 28 days (7.91 ± 2.29 ng/mL) was significantly greater than that in septic patients without shock (6.61 ± 2.56) (t=−3.37, P = 0.00) (Fig. 4a). The ROC curves of Fibulin2 (AUC 0.67, P = 0.00, 95% CI 0.59–0.75), CRP (AUC 0.48, P = 0.65, 95% CI 0.39–0.57), PCT (AUC 0.53, P = 0.65, 95% CI 0.40–0.65), WBC (AUC 0.44, P = 0.16, 95% CI 0.35–0.52), NEU% (AUC 0.52, P = 0.63, 95% CI 0.43–0.62), IL-6 (AUC 0.44, P = 0.32, 95% CI 0.31–0.57), and D-dimer (AUC 0.51, P = 0.89, 95% CI 0.40–0.61) for the diagnosis of sepsis are presented in Fig. 4b, c, d, e, f, g, and h, respectively. In other words, only Fibulin2 was a biomarker for predicting survival status in patients with sepsis. Conversely, CRP, PCT, WBC, NEU%, IL-6, and D-dimer have no diagnostic value for predicting survival status in patients with shock. The cutoff value of 6.69 ng/mL for Fibulin2 provided optimum diagnostic power by balancing the ability to predict survival status in patients with sepsis (sensitivity 72.73%) and case controls (specificity 57.78%) and had the highest Youden index, which was 0.31. Accordingly, the positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio and odds ratio were 63.27%, 67.93%, 1.72, 0.47 and 3.65, respectively.

Fig. 4.

Performance of Fibulin2 in the prediction of survival in patients with sepsis among different biomarkers. a Levels of Fibulin2 in plasma from patients with or without shock. b, c, d, e, f, g, h Receiver operating characteristic curves (ROC curve) of Fibulin2, CRP, PCT, WBC, NEU%, IL-6, and D-dimer to predict septic shock. Fibulin2 (AUC 0.67, P = 0.00, 95% Cl 0.59–0.75), CRP (AUC 0.48, P = 0.65, 95% Cl 0.39–0.57), PCT (AUC 0.53, P = 0.65, 95% Cl 0.40–0.65), WBC (AUC 0.44, P = 0.16, 95% Cl 0.35–0.52), NEU% (AUC 0.52, P = 0.63, 95% Cl 0.43–0.62), IL-6 (AUC 0.44, P = 0.32, 95% Cl 0.31–0.57), and D-dimer (AUC 0.51, P = 0.89, 95% Cl 0.40–0.61)

Discussion

Sepsis is a severe syndrome of dysregulated inflammatory reactions resulting from infection with characteristic organ dysfunction [19]. Despite progress in medical care, the mortality of patients with sepsis has ranged from 20 to 30% recent decades [20]. Early diagnosis and prognosis of sepsis are conducive to the rapid initiation of appropriate therapy, which actively affects the clinical course and reduces the mortality rate [21]. Therefore, a rapid and reliable index that can be adapted to diagnose sepsis in the emergency department and a timely and effective indicator of the short-term prognosis of sepsis patients are still needed [22, 23]. Various biomarkers, including CRP, PCT, WBC, NEU% and D-dimer, have been studied to assist in diagnosing sepsis and predicting its severity and mortality frequently [24–26]. Nevertheless, most of them are restricted. For example, some markers cannot be used to determine the prognosis sufficiently. PCT has been reported to have clinical utility in guiding antibiotic usage in the setting of lower respiratory tract infections, but its predictive and prognostic ability for sepsis is controversial [4]. Thus, new biomarkers available to estimate sepsis severity and enable earlier treatment initiation are highly anticipated [13].

As an extracellular matrix protein, Fibulin2 can be secreted by many cells, such as bone marrow stromal cell and macrophage. Fibulin2 could also take part in several pathophysiological processes including heart development, skin wound healing and cancer invasion and metastasis. We previously reported that plasma Fibulin2 levels are increased in patients with infection [11]. In this study, we focused on patients with sepsis to evaluate the differences in Fibulin2 levels between patients with septic shock and those without shock and between nonsurvivors and survivors. We studied ED patients who were diagnosed with sepsis. Blood samples were collected within 12 h of ED admission, and fibulin2 levels were detected via ELISA. A total of 462 participants, including 235 septic patients, were included. Fibulin2 levels were significantly higher in patients with sepsis than in patients without sepsis, higher in patients with septic shock than in patients with sepsis without shock, and higher in the nonsurviving group than in the surviving group. The ROC curves revealed that the AUC of Fibulin2 was 0.82 for the diagnosis of sepsis, which was greater than that of CRP, PCT, WBC, NEU%, D-dimer and IL-6. The AUC of Fibulin2 was 0.63 for diagnosing septic shock in patients with sepsis, which was greater than those of the other biomarkers above. The AUC of Fibulin2 was 0.67 for the prediction of 28-day mortality, which was greater than that of the other indices. These results suggest that Fibulin2 could be a novel diagnostic and prognostic biomarker for sepsis [13].

However, there were also several limitations in this study. First, this was a single-center clinical diagnosis study, and the sample size of participants was relatively restricted. Therefore, a larger multicenter study is needed to validate the results further. Second, we compared Fibulin2 with classical biomarkers for diagnostic and prognostic capacity in sepsis, including CRP, PCT, WBC, NEU%, D-dimer and IL-6. However, we did not compare it with other nonclassical biomarkers, such as blood platelets, creatinine, prealbumin, alanine aminotransferase, aspartate aminotransferase, bilirubin, and renin [9, 27, 28]. Future studies may compare Fibulin2 levels in the diagnosis or prognosis of sepsis. Third, there are other intricate causes of sepsis, and we did not categorize sepsis according to the pathogens. Fourth, there may be some undiscovered pathologies or disease processes could also be relevant to the levels of Fibulin2 that were not be excluded. Finally, in vitro or in vivo mechanistic experiments should be performed to investigate the diagnostic and prognostic significance of Fibulin2 in sepsis.

Conclusions

Fibulin2 can be considered both a diagnostic and a prognostic biomarker for sepsis and septic shock in the ED. Overall, Fibulin2 was superior to CRP, PCT, WBC, NEU%, IL-6, and D-dimer in diagnostic and prognostic value for sepsis and septic shock in the ED.

Authors’ contributions

FJ and LL designed the study. GXW and LSD were the major contributors to the writing of the manuscript. TDQ, FYY and ZYM collected the study data. LSD and MJF distributed the data and performed the statistical analysis. GDB, LXM and XHH evaluated the laboratory tests. XW, WSC and LYB controlled the data accuracy. DYM and WJZ supervised the study and edited the manuscript. All the authors read and approved the final manuscript.

Funding

This work was supported by the Project of the State Key Laboratory of Trauma, Burn and Combined Injury (grant number SKLZZ202201), the Chongqing Science and Technology Committee Joint Traditional Chinese Medicine Research Project (grant number 2023DBXM010), the Training Plan of Talent Innovation Ability in Army Medical Center (grantnumberZXYZZKY01), the Natural Science Foundation of Chongqing (grant number CSTB2024NSCQ-MSX0485) and the National Natural Science Foundation of China (NSFC) (grant number 82302435).

Data availability

The datasets used or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study complied with the provisions outlined in the Declaration of Helsinki and was approved by the Clinical Ethics Committee of the Army Medical Center of the Chinese People’s Liberation Army [Approval number: Medical Research Review (2021) NO 07 and Medical Research Review (2024) NO 295]. All participants or their legal representatives signed a written informed consent form before participating in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.