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.

Cell-free DNA predicts all-cause mortality of sepsis-induced acute kidney injury.

Background Sepsis-induced acute kidney injury (S-AKI) is a common complication in critically ill patients. Therefore, reliable biomarkers for predicting S-AKI outcomes are necessary. Serum cell-free DNA (cfDNA) is a circulating extracellular DNA fragment used as a noninvasive screening tool for many diseases, including sepsis. This study aimed to investigate the prognostic value of cfDNA in S-AKI patients and its relationship with some other parameters.Methods A total of 89 S-AKI patients admitted to the intensive care unit (ICU) from June 2021 to December 2021 were enrolled in this study. The patients were categorized into the low cfDNA group (< 855 ng/ml) and high cfDNA group (≥ 855 ng/ml) and were followed up for three months. CfDNA was extracted from serum and quantified using Quant-iT PicoGreen dsDNA Reagent.Results Overall survival was significantly lower in the high cfDNA group than in the low cfDNA group (Log-Rank p = 0.012). Univariate Cox proportional hazard model showed that cfDNA was significantly associated with all-cause mortality (HR [hazard ratio] 2.505, 95% CI [95% confidence interval] 1.184-5.298, p = 0.016). Also, serum cfDNA was a significant risk factor for all-cause mortality after adjusting for covariates (HR 2.191, 95% CI 1.017-4.721, p = 0.045). Moreover, cfDNA was positively correlated with several baseline parameters, including serum creatine, aspartate aminotransferase, alanine aminotransferase, prothrombin time, and International Normalized Ratio.Conclusion High serum cfDNA level is associated with higher mortality among the S-AKI population, indicating that cfDNA is a valuable biomarker for S-AKI prognosis.