BACKGROUND
The Sequential Organ Failure Assessment-2 (SOFA-2) score is a recently developed update of the conventional SOFA score that incorporates contemporary patterns of organ support in the intensive care unit (ICU). However, the association between SOFA-2 and long-term mortality in patients with sepsis has not been well characterized.
METHODS
This retrospective cohort study extracted adult sepsis ICU data from MIMIC-IV v3.1. ICU admission SOFA-2 scores (0-24) were calculated. Boruta algorithm selected confounders; binary logistic regression and restricted cubic splines (RCS) analyzed linear association between SOFA-2 and 90-/365-day all-cause mortality. Random forest (RF) combined key predictors, SHAP interpreted feature importance, and AUC evaluated discriminative performance.
RESULTS
8,779 patients were enrolled. Fully adjusted regression showed SOFA-2 independently elevated 90-day mortality (OR=1.17, 95%CI 1.15-1.18) and 365-day mortality (OR=1.16, 95%CI 1.14-1.18). RCS confirmed an approximately linear correlation (all nonlinearity p>0.05). Single SOFA-2 yielded modest AUCs: 0.65 (90-day) and 0.64 (365-day). The RF model improved prediction (validation AUC: 0.68 for 90-day, 0.67 for 365-day). SHAP analysis identified SOFA-2 as the most influential predictor.
CONCLUSIONS
SOFA-2 provides moderate, stable long-term mortality discrimination for sepsis patients. Further external validation and optimization are required to improve its risk stratification utility.
Ulcerative colitis (UC) is an inflammatory bowel disease involving complex interactions between genetics, gut microbiota, metabolism, and immunity. This study aimed to systematically evaluate multi‐omics factors potentially associated with UC susceptibility and identify reliable diagnostic biomarkers. A two‐sample Mendelian randomization (MR) framework assessed potential causal associations between gut microbiome, circulating metabolites, immune cell phenotypes, and UC susceptibility. Significant MR findings were integrated with multiple transcriptomic datasets to identify differentially expressed candidate genes. Immune infiltration analysis, machine learning modeling, and external validation were subsequently performed. Single‐cell and spatial transcriptomics were used to localize key genes and to explore their potential cell type‐specific functions within the tissue microenvironment, followed by qRT‐PCR validation in independent clinical tissues and siRNA‐mediated IFITM2 knockdown in THP‐1‐derived macrophages. MR analyses identified potential causal associations for specific microbiota, sphingomyelin‐related metabolites, and immune cell phenotypes with UC susceptibility. Integrative analysis prioritized four core signature genes: SAG, WDR48, IFITM2, and SIRPA. A random forest model achieved an AUC of 0.964 and identified a four‐gene signature with strong diagnostic performance. Single‐cell and spatial transcriptomics localized IFITM2 upregulation mainly to myeloid cells, particularly Neutrophil_IFITM2. CellChat suggested a potential CD4_Tem_IL7R‐ANXA1‐FPR1‐Neutrophil_IFITM2 axis. qRT‐PCR supported the expression directions of the four genes, and IFITM2 knockdown in THP‐1‐derived macrophages reduced TNF‐α, IL‐6, and IL‐1β mRNA expression. This multi‐omics framework supports the potential roles of specific microbiota, sphingolipid metabolism, and immune phenotypes in UC pathogenesis. The four‐gene signature and characterization of Neutrophil_IFITM2, supported by independent qRT‐PCR validation and preliminary IFITM2 knockdown experiments, may provide a framework for precision diagnosis and future mechanistic studies in UC.
Yiyun Wang, Yulin Tian, Hongsi Cui et al.· The FASEB Journal· 0 citations
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