Daily Sepsis Research Analysis
Three impactful studies advance sepsis science across mechanisms and bedside decision support. A mechanistic paper links lactate-driven HMGB1 lactylation in macrophages to NET formation via cGAS/STING, illuminating a therapeutic axis in sepsis-associated acute kidney injury. Two translational/clinical studies show promise for precision diagnosis in preterm neonates via metabolomics and for ED airway decision support in septic shock using explainable machine learning.
Summary
Three impactful studies advance sepsis science across mechanisms and bedside decision support. A mechanistic paper links lactate-driven HMGB1 lactylation in macrophages to NET formation via cGAS/STING, illuminating a therapeutic axis in sepsis-associated acute kidney injury. Two translational/clinical studies show promise for precision diagnosis in preterm neonates via metabolomics and for ED airway decision support in septic shock using explainable machine learning.
Research Themes
- Immunometabolism and DAMP signaling (lactate-HMGB1-cGAS/STING) in sepsis
- Precision diagnostics for neonatal sepsis using metabolomics
- Machine learning decision support for airway management in septic shock
Selected Articles
1. Lactate-induced macrophage HMGB1 lactylation promotes neutrophil extracellular trap formation in sepsis-associated acute kidney injury.
In human SAKI and murine CLP models, elevated lactate drove HMGB1 accumulation and lactylation in macrophages, enhancing NET formation and worsening kidney injury via cGAS/STING activation. Exosome-mediated HMGB1 transfer from macrophages to neutrophils linked immunometabolism to innate effector activation, nominating HMGB1 lactylation as a therapeutic target.
Impact: Identifies a lactate–HMGB1–cGAS/STING axis that mechanistically links sepsis immunometabolism to NET-driven organ injury and offers a druggable node. The integration of human samples, mouse models, and mechanistic assays strengthens translational relevance.
Clinical Implications: Therapeutic strategies that reduce HMGB1 lactylation, block HMGB1 signaling, modulate lactate handling, or inhibit cGAS/STING may mitigate SAKI. Lactate levels could serve as a biomarker to stratify risk and guide such interventions.
Key Findings
- Elevated blood lactate correlated with increased HMGB1 in SAKI patients.
- Lactate increased HMGB1 expression, promoted NET formation, and worsened kidney injury in CLP mice.
- Lactate induced HMGB1 lactylation in macrophages and exosomal HMGB1 transfer, triggering neutrophil mtDNA release and cGAS/STING activation.
Methodological Strengths
- Translational design spanning human samples, in vivo CLP model, and in vitro co-culture.
- Mechanistic dissection with exosome isolation, co-immunoprecipitation, immunofluorescence, ELISA, and Western blotting.
Limitations
- Human component appears observational with limited sample characterization.
- No interventional blockade of HMGB1/lactylation or cGAS/STING in patients; clinical efficacy remains untested.
Future Directions: Evaluate pharmacologic inhibitors of HMGB1 lactylation or cGAS/STING in preclinical SAKI models; prospectively validate lactate/HMGB1 as biomarkers and test targeted therapies in early-phase clinical trials.
2. Survival of the Littlest: Navigating Sepsis Diagnosis beyond Inflammation in Preterm Neonates.
Targeted metabolomics of 227 samples from 94 preterm neonates revealed shared and progressive metabolic shifts from control to SINS to sepsis, with sex- and pathogen-specific signatures. A 5-metabolite plus IL-6 model distinguished SINS from sepsis at clinical suspicion (AUC 0.79; sensitivity 0.85; specificity 0.82), supporting precision diagnostics to guide antibiotics.
Impact: Provides an explainable, biologically anchored metabolomic panel for early neonatal sepsis discrimination, addressing overuse of antibiotics and diagnostic delays in a vulnerable population.
Clinical Implications: If validated externally, the metabolite+IL-6 panel could triage preterm neonates at suspicion, reducing unnecessary antibiotics while expediting treatment for true sepsis. Sex- and pathogen-specific signatures support personalized care.
Key Findings
- Metabolic changes were shared between SINS and sepsis and progressed from control to SINS to sepsis.
- Distinct sex-specific (male pro-inflammatory, female anti-inflammatory) and pathogen-specific metabolic profiles were observed.
- A panel of 5 metabolic features plus IL-6 discriminated SINS from sepsis at clinical suspicion (AUC 0.79; sensitivity 0.85; specificity 0.82).
Methodological Strengths
- Comprehensive LC–MS profiling with linear mixed-effects modeling, LASSO, and logistic regression.
- Biologically plausible stratification by sex and pathogen, and analysis across multiple clinical states (control, SINS, sepsis).
Limitations
- Observational case-control design with moderate sample size and no external validation; potential overfitting risk.
- Single healthcare setting limits generalizability; timing of sampling relative to clinical events may vary.
Future Directions: Multicenter external validation and prospective impact studies integrating the panel into neonatal sepsis pathways; exploration of pathogen- and sex-tailored thresholds and therapeutic modulation of implicated metabolic pathways.
3. Using machine learning techniques for early prediction of tracheal intubation in patients with septic shock: a multi-center study in South Korea.
Across 21 EDs (n=4,762), an XGBoost model predicted 24-hour intubation in septic shock with AUROC 0.829 and F1 0.654. SHAP analysis highlighted post-fluid lactate, suspected lung infection, initial pH, SOFA, and respiratory rate as key drivers, enabling explainable risk stratification at triage.
Impact: Delivers a scalable, explainable ML tool for early airway decision support in septic shock, using routinely available variables and multicenter data.
Clinical Implications: Could be integrated into ED workflows to flag high-risk patients for early airway planning, resource allocation, and closer monitoring while maintaining transparency via SHAP explanations.
Key Findings
- In 4,762 septic shock patients, 31% required intubation within 24 hours; XGBoost achieved AUROC 0.829 and F1 0.654.
- Top predictive features included lactate after initial fluids, suspected lung infection, initial pH, SOFA score, and respiratory rate.
- Model development used stratified five-fold cross-validation and grid search, with SHAP values for interpretability.
Methodological Strengths
- Large, multicenter dataset with stratified cross-validation and explainability via SHAP.
- Use of routinely available ED features supports real-world deployment.
Limitations
- Retrospective design and lack of prospective external validation; clinical thresholds and workflow integration remain untested.
- Intubation decisions are clinician-dependent, introducing indication bias.
Future Directions: Prospective, external validation and randomized or stepped-wedge implementation studies to test clinical impact; calibration for different health systems and integration into ED EHRs.