Circulating Pulmonary-Originated Epithelial Biomarkers for Acute Respiratory Distress Syndrome: A Systematic Review and Meta-Analysis

LIPS and PaO2/FiO2 Combined Plasma Biomarkers Predict Onset of Acute Respiratory Distress Syndrome in Patients of High Risks in SICU: A Prospective Exploratory Study

Objective

To explore and validate the value of clinical parameters combined with plasma biomarkers for predicting acute respiratory distress syndrome (ARDS) in patients of high risks in the surgical intensive care unit (SICU).

Materials and Methods

We conducted a prospective, observational study from January 2020 to December 2023, which enrolled 263 patients of high risks in the SICU of Peking University Third Hospital consecutively; they were classified as ARDS and non-ARDS according to whether ARDS occurred after enrollment. Collected clinical characteristics and blood samples within 24 hr of admission to SICU. Blood samples from the first day to the seventh day of SICU were collected from patients without ARDS, and patients with ARDS were collected until 1 day after ARDS onset, forming data based on time series. ELISA and CBA were used to measure plasma biomarkers. Endpoint of the study was the onset of ARDS. Cox proportional hazard regression analysis was used to find independent risk factors of the onset of ARDS, then constructed a nomogram and tested its goodness-of-fit.

Results

About 84 of 263 patients ended with ARDS. Univariate analysis found 15 risk factors showed differences between ARDS and non-ARDS, namely, interleukin 6, interleukin 8 (IL-8), angiopoietin Ⅱ, LIPS, APACHEⅡ, SOFA, PaO2/FiO2, age, sex, shock, sepsis, acute abdomen, pulmonary contusion, pneumonia, hepatic dysfunction. We included factors with p  < 0.2 in multivariate analysis and showed LIPS, PaO2/FiO2, IL-8, and receptor for advanced glycation end-products (RAGE) of the first day were independent risk factors for ARDS in SICU, a model combining them was good in predicting ARDS (C-index was 0.864 in total patients of high risks). The median of the C-index was 0.865, showed by fivefold cross-validation in the train cohort or validation cohort. The calibration curve shows an agreement between the probability of predicting ARDS and the actual probability of occurrence. Decision curve analysis indicated that the model had clinical use value. We constructed a nomogram that had the ability to predict ARDS in patients of high risks in SICU.

Conclusions

LIPS, PaO2/FiO2, plasma IL-8, and RAGE of the first day were independent risk factors of the onset of ARDS. The predictive ability for ARDS can be greatly improved when combining clinical parameters and plasma biomarkers.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS

BACKGROUND

The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified.

METHODS

We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis.

RESULTS

In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P  = 0.008).

INTERPRETATION

Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

Diagnostic Performance of Plasma SP-D, KL-6, and CC16 in Acutely Hospitalised Patients Suspected of Having Community-Acquired Pneumonia-A Diagnostic Accuracy Study

Community-acquired pneumonia is a common cause of acute hospitalisation. Identifying patients with community-acquired pneumonia among patients suspected of having the disease can be a challenge, which causes unnecessary antibiotic treatment. We investigated whether the circulatory pulmonary injury markers surfactant protein D (SP-D), Krebs von den Lungen-6 (KL-6), and Club cell protein 16 (CC16) could help identify patients with community-acquired pneumonia upon acute admission. In this multi-centre diagnostic accuracy study, SP-D, KL-6, and CC16 were quantified in plasma samples from acutely hospitalised patients with provisional diagnoses of community-acquired pneumonia. The area under the receiver operator characteristics curve (AUC) was calculated for each marker against the following outcomes: patients' final diagnoses regarding community-acquired pneumonia assigned by an expert panel, and pneumonic findings on chest CTs. Plasma samples from 339 patients were analysed. The prevalence of community-acquired pneumonia was 63%. AUCs for each marker against both final diagnoses and chest CT diagnoses ranged between 0.50 and 0.56. Thus, SP-D, KL-6, and CC16 demonstrated poor diagnostic performance for community-acquired pneumonia in acutely hospitalised patients. Our findings indicate that the markers cannot readily assist physicians in confirming or ruling out community-acquired pneumonia.

Research progress of KL-6 in respiratory system diseases

This article comprehensively elucidates the discovery of Krebs von den Lungen-6 (KL-6), its structural features, functional mechanisms, and the current research status in various respiratory system diseases. Discovered in 1985, KL-6 was initially considered a tumor marker, but its elevated levels in interstitial lung disease (ILD) led to its recognition as a relevant serum marker for ILD. KL-6 is primarily produced by type 2 alveolar epithelial cell regeneration. Over the past 30 years since the discovery of KL-6, the number of related research papers has steadily increased annually. Following the coronavirus disease 2019 (COVID-19) pandemic, there has been a sudden surge in relevant literature. Despite KL-6's potential as a biomarker, its value in the diagnosis, treatment, and prognosis varies across different respiratory diseases, including ILD, idiopathic pulmonary fibrosis (IPF), COVID-19, and lung cancer. Therefore, as an important serum biomarker in respiratory system diseases, the value of KL-6 still requires further investigation.

Prone Positioning and Molecular Biomarkers in COVID and Non-COVID ARDS: A Narrative Review

Prone positioning (PP) represents a therapeutic intervention with the proven capacity of ameliorating gas exchanges and ventilatory mechanics indicated in acute respiratory distress syndrome (ARDS). When PP is selectively applied to moderate-severe cases of ARDS, it sensitively affects clinical outcomes, including mortality. After the COVID-19 outbreak, clinical application of PP peaked worldwide and was applied in 60% of treated cases, according to large reports. Research on this topic has revealed many physiological underpinnings of PP, focusing on regional ventilation redistribution and the reduction of parenchymal stress and strain. However, there is a lack of evidence on biomarkers behavior in different phases and phenotypes of ARDS. Patients response to PP are, to date, decided on PaO2/FiO2 ratio improvement, whereas scarce data exist on biomarker tracking during PP. The purpose of this review is to explore current evidence on the clinical relevance of biomarkers in the setting of moderate-severe ARDS of different etiologies (i.e., COVID and non-COVID-related ARDS). Moreover, this review focuses on how PP may modulate biomarkers and which biomarkers may have a role in outcome prediction in ARDS patients.

ACE2 and a Traditional Chinese Medicine Formula NRICM101 Could Alleviate the Inflammation and Pathogenic Process of Acute Lung Injury

COVID-19 is a highly transmittable respiratory illness caused by SARS-CoV-2, and acute lung injury (ALI) is the major complication of COVID-19. The challenge in studying SARS-CoV-2 pathogenicity is the limited availability of animal models. Therefore, it is necessary to establish animal models that can reproduce multiple characteristics of ALI to study therapeutic applications. The present study established a mouse model that has features of ALI that are similar to COVID-19 syndrome to investigate the role of ACE2 and the administration of the Chinese herbal prescription NRICM101 in ALI. Mice with genetic modifications, including overexpression of human ACE2 (K18-hACE2 TG) and absence of ACE2 (mACE2 KO), were intratracheally instillated with hydrochloric acid. The acid intratracheal instillation induced severe immune cell infiltration, cytokine storms, and pulmonary disease in mice. Compared with K18-hACE2 TG mice, mACE2 KO mice exhibited dramatically increased levels of multiple inflammatory cytokines (IL-6 and TNF-α) in bronchoalveolar lavage fluid, histological evidence of lung injury, and dysregulation of MAPK and MMP activation. In mACE2 KO mice, NRICM101 could ameliorate the disease progression of acid-induced ALI. In conclusion, the established mouse model provided an effective platform for researchers to investigate pathological mechanisms and develop therapeutic strategies for ALI, including COVID-19-related ALI.

Machine learning-based identification of cuproptosis-related markers and immune infiltration in severe community-acquired pneumonia

BACKGROUND

Severe community-acquired pneumonia (SCAP) is one of the world's most common diseases and a major etiology of acute respiratory distress syndrome (ARDS). Cuproptosis is a novel form of regulated cell death that can occur in various diseases.

METHODS

Our study explored the degree of immune cell infiltration during the onset of severe CAP and identified potential biomarkers related to cuproptosis. Gene expression matrix was obtained from GEO database indexed GSE196399. Three machine learning algorithms were applied: The least absolute shrinkage and selection operator (LASSO), the random forest, and the support vector machine-recursive feature elimination (SVM-RFE). Immune cell infiltration was quantified by single-sample gene set enrichment analysis (ssGSEA) scoring. Nomogram was constructed to verify the applicability of using cuproptosis-related genes to predict the onset of severe CAP and its deterioration toward ARDS.

RESULTS

Nine cuproptosis-related genes were differentially expressed between the severe CAP group and the control group: ATP7B, DBT, DLAT, DLD, FDX1, GCSH, LIAS, LIPT1, and SLC31A1. All 13 cuproptosis-related genes were involved in immune cell infiltration. A three-gene diagnostic model was constructed to predict the onset of severe CAP: GCSH, DLD, and LIPT1.

CONCLUSION

Our study confirmed the involvement of the newly discovered cuproptosis-related genes in the progression of SCAP.