Inflammatory biomarkers associated with infection-related type I acute respiratory failure

Kairui Ren; Jiatong Xu; Ya Wang; Yan Li; Huadong Zhu

doi:10.5847/wjem.j.1920-8642.2026.049

. 2026 May 1;17(3):237–243. doi: 10.5847/wjem.j.1920-8642.2026.049

Inflammatory biomarkers associated with infection-related type I acute respiratory failure

Kairui Ren ¹, Jiatong Xu ¹, Ya Wang ¹, Yan Li ¹, Huadong Zhu ^1,^✉

PMCID: PMC13199136 PMID: 42199761

Abstract

BACKGROUND:

Acute respiratory failure (ARF) is a heterogeneous syndrome in which early differentiation between infection-related and non-infection-related etiologies remains challenging. This study aimed to characterize the inflammatory profile of type I ARF and identify candidate biomarkers associated with the infection-related type I ARF.

METHODS:

In this prospective observational cohort study, 98 patients with type I ARF and 30 healthy controls were enrolled. The plasma levels of 92 inflammation-related proteins were measured using the Olink Inflammation Panels. Type I ARF-associated biomarkers were first identified by comparing type I ARF patients with healthy controls, followed by the identification of infection-related biomarkers within the type I ARF cohort. False discovery rate correction and random forest-based feature ranking were applied.

RESULTS:

Compared with non-infection-related type I ARF (NonInf-ARF), infection-related type I ARF (Inf-ARF) was associated with a higher incidence of acute respiratory distress syndrome (ARDS) and greater use of advanced life-support therapies. A comparison with healthy controls identified 64 significantly dysregulated biomarkers, representing the inflammatory features of type I ARF. Twenty of the 64 type I ARF-associated biomarkers remained significantly different between the Inf-ARF and NonInf-ARF groups (q<0.05). Neurotrophin-3 (NT-3), interleukin-18 receptor 1 (IL-18R1), interleukin-18 (IL-18), and C-X-C motif chemokine ligand 10 (CXCL10) consistently ranked among the top features across the different analytical methods.

CONCLUSION:

NT-3, IL-18R1, IL-18, and CXCL10 were identified as candidate biomarkers associated with the Inf-ARF, which may provide additional information for early etiologic stratification.

Keywords: Acute respiratory failure, Type I, Inflammatory biomarkers, Subphenotype

INTRODUCTION

Type I acute respiratory failure (ARF), also known as hypoxemic respiratory failure, is a life-threatening condition frequently encountered in emergency and critical care settings and characterized by inadequate oxygenation of the blood. It is often associated with conditions such as pneumonia, acute respiratory distress syndrome (ARDS), and severe asthma.^[1,2] Therefore, early identification of the underlying cause is essential for guiding targeted therapy and improving outcomes.

Type I ARF is heterogeneous. Infection-related type I ARF (Inf-ARF) and non-infection-related type I ARF (NonInf-ARF) differ in pathophysiology, organ involvement, and treatment. Inf-ARF is usually triggered by pathogen-associated molecular patterns (PAMPs) and is often accompanied by the release of specific damage-associated molecular patterns (DAMPs), leading to a significant early systemic inflammatory response that may coexist with compensatory anti-inflammatory signals.^[3] In contrast, NonInf-ARF is more often caused by physical or chemical injury, and its pathogenic mechanism may be more closely related to aseptic inflammation, tissue damage repair processes, and the activation of remodeling-related pathways.^[4] These different biological processes suggest that the two subphenotypes may exhibit different etiologies.

At present, etiologic assessment of type I ARF relies mainly on clinical judgment, imaging, and microbiological testing of blood or bronchoalveolar lavage fluid. However, microbiological results are often delayed, and early clinical manifestations or routine laboratory findings may lack sufficient specificity, which may create a critical diagnostic window of uncertainty. During this stage, misclassification may cause inappropriate antimicrobial use or delayed disease-specific treatment.^[5,6] Therefore, there is an urgent need for reliable biomarkers to reflect underlying biological differences and to aid early etiological stratification.

Inflammatory profiling can reveal biologically meaningful subphenotypes in critically ill patients, particularly those with ARDS, however, most studies have focused on prognostic stratification rather than etiologic classification.^[7-9] In this study, we performed high-throughput inflammation analysis on plasma samples using the Olink platform to identify candidate biomarkers associated with Inf-ARF.

METHODS

Study design and population

This prospective observational cohort study enrolled ARF patients in the emergency intensive care unit (EICU) of Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, China, between May 2022 and July 2024. Those who meeting the criteria for type I ARF were enrolled as study subjects: involves hypoxaemia (PaO₂ <60mmHg [1 mmHg=0.133 kPa]) with normocapnia (PaCO₂ <45 mmHg). Patients younger than 18 years, pregnant individuals, with type II ARF, ventilated for more than 72 h prior to enrollment, or with incomplete clincial infornation, were excluded. Thirty healthy individuals from the institutional health examination center served as controls. A detailed flowchart of patient screening and inclusion is shown in Figure 1.

The etiologies of ARF included pneumonia, sepsis, congestive heart failure (CHF), acute exacerbation of interstitial pneumonia, and pulmonary embolism.

Data collection

Baseline clinical data, including demographics, comorbidities, mechanical ventilation and laboratory variables, and Sequential Organ Failure Assessment (SOFA) scores at admission, were recorded.

Definition of classification

Patients were classified into two etiological groups: Inf-ARF,^[6] was defined by either (1) positive microbiological results (culture, polymerase chain reaction, or antigen test) from normally sterile body fluids (blood, pleural fluid) or lower respiratory tract specimens (bronchoalveolar lavage, protected specimen brush); or (2) clinically diagnosed infection: community-acquired pneumonia (CAP) needed to be admitted to the intensive care unit (ICU) within 48 h of presentation, diagnosed by compatible clinical features and new-onset infiltrate on chest imaging of presumed infectious origin;^[7] or (3) sepsis meeting the Sepsis-3.0 definition, with acute organ dysfunction (ΔSOFA≥2) attributable to infection.

NonInf-ARF:^[8,9] was defined as a confirmed non-infectious etiology (e.g., acute heart failure confirmed by echocardiography and elevated N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels or acute exacerbation of interstitial lung disease) without evidence of infection within 72 h of hospitalization. Patients who developed new infections after this window were still classified as NonInf-ARF.

Biomarker analysis

Blood samples from type I ARF patients and healthy controls were drawn for biomarker analysis within 1 h of admission to the EICU prior to the administration of broad-spectrum antibiotics or cardiopulmonary interventions.

Human plasma was centrifuged at 1,500 g (4 °C) for 15 min within 2 h after collection and analyzed using Olink Inflammation Panels. The final quantification was performed on the Fluidigm qPCR platform by Shanghai Applied Protein Technology Co., Ltd. (Shanghai, China). Protein levels were expressed as normalized protein expression (NPX), a log₂-scaled relative unit.

Inflammatory biomarker screening and feature ranking

To assess the inflammatory profile of type I ARF, the plasma levels of the 92 Olink biomarkers were compared between type I ARF patients (n=98) and healthy controls (n=30) using the Wilcoxon rank-sum test. Multiple testing was adjusted by the Benjamini-Hochberg false discovery rate (FDR) method, and biomarkers with q<0.05 were considered significantly altered, constituting the type I ARF-associated inflammatory profile.

To identify candidate biomarkers associated with Inf-ARF, a second comparative analysis was performed within the type I ARF cohort. The random forest (RF) algorithm was used to rank their importance in distinguishing Inf-ARF from NonInf-ARF.

Statistical analyses

All statistical analyses were performed with SPSS (version 26.0, IBM, USA). Continuous variables are presented as the mean±standard deviation, and categorical variables are presented as frequencies and percentages. Initial comparisons of baseline characteristics were conducted using the Kruskal-Wallis test and Wilcoxon rank-sum test (Mann-Whitney U test) for continuous variables and Fisher’s exact test for categorical variables. A two-sided P-value < 0.05 was considered to indicate statistical significance. All advanced statistical and machine-learning analyses were performed using R software (version 4.4.2; R Foundation for Statistical Computing).

RESULTS

Clinical characteristics

Among 141 ARF patients, 98 met the criteria for type I ARF, including 73 with Inf-ARF and 25 with NonInf-ARF. Comparisons of baseline characteristics and clinical outcomes by ARF etiology are summarized in supplementary Table 1 and supplementary Figure 1.

Comparisons of baseline variables between the Inf-ARF and NonInf-ARF groups are shown in supplementary Table 2. At admission, the oxygenation indices of the two groups were similar. However, the clinical course of Inf-ARF was more severe, with significantly lower worst partial pressure of oxygen (PaO₂)/fraction of inspired oxygen (FiO₂) ratios, a higher incidence of ARDS (84.9% vs. 32.0%, P<0.001), and a greater need for invasive mechanical ventilation (76.7% vs. 28.0%, P<0.001). Although in-hospital mortality did not differ significantly, Inf-ARF showed a more severe clinical trajectory (Table 1).

Table 1.

Comparisons of clinical outcomes between the two subphenotypes of type I ARF

Clinical outcomes	NonInf-ARF (n=25)	Inf-ARF (n=73)	P-value
Worst PaO₂/FiO₂ ratio, median (IQR)	210 (168.0, 237.5)	139 (114.4, 199.0)	0.002
ARDS, n (%)	8 (32.0)	62 (84.9)	<0.001
Invasive mechanical ventilation, n (%)	7 (28.0)	56 (76.7)	<0.001
In-hospital mortality, n (%)	6 (24.0)	23 (31.5)	0.478

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ARF: acute respiratory failure; ARDS: acute respiratory distress syndrome; PaO₂: partial pressure of arterial oxygen; FiO₂: fraction of inspired oxygen; IQR: interquartile range; NonInf-ARF: non-infection-related type I ARF; Inf-ARF: infection-related type I ARF.

Biomarkers related to type I ARF

A systematic screening of 92 plasma inflammatory biomarkers was conducted using the Olink Inflammation Panels to identify biomarkers associated with type I ARF (supplementary Table 3). A comparison with healthy controls revealed 64 significantly dysregulated biomarkers, representing the inflammatory features of type I ARF (supplementary Table 4).

Candidate biomarkers associated with infection-related type I ARF

After the second-round FDR correction, 20 of the 64 type I ARF-associated biomarkers remained significantly different between the Inf-ARF and NonInf-ARF groups (q<0.05; supplementary Table 5). To prioritize the selection of candidate biomarkers with the highest discriminative power, RF analysis was performed with ten-fold cross-validation using the 20 biomarkers as input features. Variable importance was assessed by the mean decrease in accuracy. The top-ranked biomarkers were monocyte chemotactic protein-3 (MCP3), interleukin-18 receptor 1 (IL-18R1), C-X-C motif chemokine ligand 10 (CXCL10), neurotrophin-3 (NT-3), and interleukin-18 (IL-18) (Figure 2A).

Additional analyses using alternative reference groups and ARDS stratification

To assess the robustness of our findings, we redefined the comparison between Inf-ARF and a composite reference group combining NonInf-ARF and healthy controls and then repeated the same differential screening procedure. After FDR correction, 51 biomarkers remained significantly different (q<0.05). In this comparison, IL-18R1, CXCL10, NT-3, and IL-18 remained the top-ranked features (Figure 2B).

When the Inf-ARF and NonInf-ARF groups were compared with the healthy control group, both groups showed extensive inflammatory dysregulation, but the deviation was more obvious in Inf-ARF (Figure 3; supplementary Tables 6 and 7). IL-18 and NT-3 were altered in both comparisons, whereas IL-18R1 and CXCL10 were significantly altered only in the Inf-ARF versus healthy control comparison.

Given the potential influence of ARDS on inflammatory markers, an additional ARDS-stratified analysis was performed within the type I ARF cohort. IL-18, IL-18R1, and NT-3 differed significantly between patients with and without ARDS, whereas CXCL10 did not (Figure 4).

DISCUSSION

This study demonstrated that type I ARF exhibits significant heterogeneity according to etiology. Inf-ARF is associated with a more severe clinical course and distinct inflammatory profiles.

CXCL10 is a member of the chemokine family and plays key roles in leukocyte recruitment during infection and inflammation. CXCL10 acts as a chemoattractant for activated T cells and monocytes via its receptor, CXCR3, and has been implicated in a variety of infectious and inflammatory conditions.^[10-14] In COVID-19-related ARDS, elevated CXCL10 concentrations in plasma and epithelial lining fluid were independently associated with a longer duration of mechanical ventilation,^[15] and CXCL10 neutralization attenuates lung injury.^[16]

NT-3 is traditionally known for its role in neuronal development. A study has demonstrated that neurotrophins, including NT-3, are secreted by subsets of immune cells^[17] and can regulate chemotaxis and inflammatory signaling pathways.^[18] NT-3 expression has been observed in fibrotic lung tissues and may influence fibroblast activity and inflammatory cell recruitment.^[19] Furthermore, NT-3 expression appears to respond to pathogen exposure: infection with Streptococcus pneumoniae downregulates NT-3 mRNA in neural tissues,^[20] indicating that infection stimulation can modulate its expression. Although its role in ARF remains unclear, NT-3 may reflect host-response changes related to neuroimmune crosstalk or repair processes in Inf-ARF.

IL-18 and its receptor IL-18R1 are central components of the inflammasome pathway, linking the recognition of pathogens by the innate immune system to Th1-type responses.^[21] Named for its ability to induce interferon-gamma (IFNγ), IL-18 exerts its immunostimulatory effects by binding to a heterodimeric receptor complex composed of IL-18Rα and IL-18Rβ chains, thereby activating innate lymphoid cells and antigen-experienced T cells.^[22] Multiple respiratory viruses, including SARS-CoV-1, SARS-CoV-2, Middle East respiratory syndrome coronavirus (MERS-CoV), and influenza A virus, have been shown to trigger apoptosis-associated speck-like protein containing a C-terminal caspase recruitment domain (ASC) speck formation, IL-18 release, and pyroptosis in infected patients.^[23] IL-18R1 expression is also elevated in severe viral infections, including influenza and COVID-19, where it is correlated with disease severity and inversely correlated with cytotoxic gene signatures.^[24] Genetic variants in IL-18R1 have also been associated with bronchopulmonary dysplasia in premature infants,^[25] further implicating this pathway in susceptibility to lung injury. The co-occurrence of IL-18 and its receptor among our candidate biomarkers suggests that activation of the IL-18/IL-18R1 axis in the infection-related phenotype potentially drives Th1 polarization and contributes to the inflammatory cascade.

Despite its established role in systemic inflammation, IL-6 was not a key candidate biomarker in the present study. In our cohort, IL-6 differed significantly between healthy controls and patients with type I ARF but not between Inf-ARF and NonInf-ARF, suggesting that it may primarily reflect general inflammatory activation rather than etiology-specific processes. This may be partly because patients with NonInf-ARF may also have a significant inflammatory response, which reduces the differential value of IL-6.

Notably, comparisons with healthy controls suggest that inflammatory dysregulation is a common feature of type I ARF, but the pattern and extent of dysregulation vary depending on the etiology. IL-18 and NT-3 are altered in both Inf-ARF and NonInf-ARF, supporting the idea that they may reflect common biological processes associated with ARF. In contrast, the expression of IL-18R1 and CXCL10 appears to be prominently altered in Inf-ARF, suggesting that these markers may reflect other molecular features associated with infection-related lung injury.

In further stratified analysis of ARDS patients, the levels of IL-18, IL-18R1, and NT-3 were significantly different between ARDS patients and non-ARDS patients, whereas the level of CXCL10 was not significantly different. These findings suggest that the levels of some candidate markers may also reflect lung injury severity, whereas the level of CXCL10 may be less strongly affected by ARDS status.

Limitations and future directions

This study has several limitations. First, this was a single-center prospective observational study with a relatively small sample size and an imbalanced distribution between the Inf-ARF and NonInf-ARF groups, which may have introduced selection bias and limited statistical robustness. In addition, the lack of an independent multicenter external validation cohort may restrict the generalizability of the findings. Second, the two groups were derived from real-world clinical populations with different underlying diseases. The Inf-ARF group mainly included patients with pneumonia and sepsis, whereas the NonInf-ARF group mainly included patients with CHF, acute exacerbation of interstitial lung disease, and pulmonary embolism. Therefore, some of the observed molecular differences may reflect differences in the underlying disease composition rather than only differences in infection status. Third, infection was defined using a comprehensive framework that included microbiological evidence, clinical diagnosis of pneumonia, and Sepsis-3 criteria. While this approach is clinically useful, it may also introduce heterogeneity in group assignment. Finally, the biological roles of the identified candidate biomarkers in type I ARF remain to be clarified, and their expression may be influenced by both infection status and inflammation severity. Further studies are needed to evaluate their stability across different pathogens and broader clinical settings.

CONCLUSION

Using the Olink Inflammation Panels for inflammatory profiling analysis, NT-3, IL-18R1, IL-18, and CXCL10 were identified as candidate biomarkers associated with the infection-related type of type I ARF. These findings suggest that different etiological subtypes of ARF may have distinct molecular characteristics and may provide additional molecular clues for early etiological stratification. Future research should focus on larger-scale, multicenter cohort studies to validate these candidate biomarkers.

Funding: This study was supported by the National High Level Hospital Clinical Research Funding (2022-PUMCH-D-005).

Ethical approval: The study was approved by the Institutional Review Board of Peking Union Medical College Hospital (I-23PJ736).

Conflicts of interest: The authors declare that there are no conflicts of interest.

Contributors: All authors contributed to the study conception and design. YW and JTX contributed to data generation and/or collection. KRR and JTX contributed to data analysis. KRR contributed to drafting of manuscript. All authors contributed to editing and approval of manuscript.

All the supplementary files in this paper are available at http://wjem.com.cn.

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[b1] 1. Cannon J, Pamplin J, Zonies D, Mason P, Sine C, Cancio L, et al. . Acute respiratory failure. Mil Med. 2018; 183(suppl_2):123-9. [DOI] [PubMed] [Google Scholar]

[b2] 2. Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. . Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA. 2016; 315(8): 788. [DOI] [PubMed] [Google Scholar]

[b3] 3. Cicchinelli S, Pignataro G, Gemma S, Piccioni A, Picozzi D, Ojetti V, et al. . PAMPs and DAMPs in sepsis: a review of their molecular features and potential clinical implications. Int J Mol Sci. 2024; 25(2): 962. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b4] 4. Samarelli AV, Tonelli R, Marchioni A, Bruzzi G, Gozzi F, Andrisani D, et al. . Fibrotic idiopathic interstitial lung disease: the molecular and cellular key players. Int J Mol Sci. 2021; 22(16): 8952. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b5] 5. Mokart D, Lambert J, Schnell D, Fouché L, Rabbat A, Kouatchet A, et al. . Delayed intensive care unit admission is associated with increased mortality in patients with cancer with acute respiratory failure. Leuk Lymphoma. 2013; 54(8): 1724-9. [DOI] [PubMed] [Google Scholar]

[b6] 6. Jurado-Palomo J, Martín-Conty JL, Polonio-López B, Picón CR, López-Izquierdo R, Vegas CDP, et al. . Prehospital critical care drug-therapy and 30-day mortality in patients with acute respiratory disease. World J Emerg Med. 2025; 16(1):43-50. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b7] 7. Renard Triché L, Fosset M, Jabaudon M, Futier E, Lucas B, Latroche C, et al. . Temporal stability of inflammatory subphenotypes of acute respiratory distress syndrome: 28-day insights from the ICAR trial. Anaesth Crit Care Pain Med. 2025; 44(5): 101559. [DOI] [PubMed] [Google Scholar]

[b8] 8. Das SK, Choupoo NS, Rochwerg B, Goswami D, Ray S, Gupta A, et al. . The impact of inflammatory biomarker subphenotypes on acute respiratory distress syndrome prognosis: a systematic review and meta-analysis. Indian J Crit Care Med. 2025; 29(7): 597-603. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b9] 9. Sarma A, Christenson SA, Shoshana BZ, Oliveira AP, Neyton LPA, Mick E, et al. . Acute respiratory distress syndrome molecular phenotypes have distinct lower respiratory tract transcriptomes. Am J Respir Crit Care Med. 2025; 211(12):2352-62. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b10] 10. Rifai M, Bergounioux J, Sleiman N, Ismail J, Zini J. . Infectious acute respiratory failure in patients under 5 years of age: a retrospective cohort study. BMJ Paediatr Open. 2024; 8(1):e002614. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b11] 11. Aliberti S, Dela Cruz CS, Amati F, Sotgiu G, Restrepo MI. . Community-acquired pneumonia. Lancet. 2021; 398(10303): 906-19. [DOI] [PubMed] [Google Scholar]

[b12] 12. Kim HS, Ha SO, Han SJ, Kim HS, Lee SH, Jung KS, et al. . Extracorporeal membrane oxygenation support in trauma versus nontrauma patients with noninfectious acute respiratory failure. Artif Organs. 2017; 41(5): 431-9. [DOI] [PubMed] [Google Scholar]

[b13] 13. Brinker A, Cheng C, Chan V. . Association of noninfectious pneumonia with ustekinumab use. JAMA Dermatol. 2019; 155(2): 221-4. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b14] 14. Elemam NM, Talaat IM, Maghazachi AA. . CXCL 10 chemokine: a critical player in RNA and DNA viral infections. Viruses. 2022; 14(11): 2445. [DOI] [PMC free article] [PubMed] [Google Scholar]

[b15] 15. Elemam NM, Khalil BA, Maghazachi AA. . Chemokines and chemokine receptors. Encyclopedia of Infection and Immunity. Amsterdam: Elsevier; 2022: 193-205. [Google Scholar]

[b16] 16. Lee EY, Lee ZH, Song YW. . CXCL10 and autoimmune diseases. Autoimmun Rev. 2009; 8(5): 379-83. [DOI] [PubMed] [Google Scholar]

[b17] 17. Lockhart-Bouron M, Vanel N, Levy M, Briant AR, Javouhey E, Breinig S, et al. . Severe acute respiratory syndrome coronavirus-2-related and imputable deaths in children: results from the French pediatric national registry. World J Pediatr. 2024; 20(6):611-20. [DOI] [PubMed] [Google Scholar]

[b18] 18. Samah B, Porcheray F, Dereuddre-Bosquet N, Gras G. . Nerve growth factor stimulation promotes CXCL-12 attraction of monocytes but decreases human immunodeficiency virus replication in attracted population. J NeuroVirology. 2009; 15(1): 71-80. [DOI] [PubMed] [Google Scholar]

[b19] 19. Ricci A, Graziano P, Bronzetti E, Saltini C, Sciacchitano S, Cherubini E, et al. . Increased pulmonary neurotrophin protein expression in idiopathic interstitial pneumonias. Sarcoidosis Vasc Diffuse Lung Dis. 2007; 24(1):13-23. [PubMed] [Google Scholar]

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PERMALINK

Inflammatory biomarkers associated with infection-related type I acute respiratory failure

Kairui Ren

Jiatong Xu

Ya Wang

Yan Li

Huadong Zhu