Daily Sepsis Research Analysis
Three impactful studies advanced sepsis research today: trajectory-based coagulation subphenotypes linked to clinical outcomes, multi-omics identification of a glycolysis–immune axis with validated hub genes, and a post hoc RCT analysis showing vitamin C does not influence platelet counts in septic shock. Together, they refine risk stratification, suggest metabolic targets, and clarify mechanisms behind a debated adjunctive therapy.
Summary
Three impactful studies advanced sepsis research today: trajectory-based coagulation subphenotypes linked to clinical outcomes, multi-omics identification of a glycolysis–immune axis with validated hub genes, and a post hoc RCT analysis showing vitamin C does not influence platelet counts in septic shock. Together, they refine risk stratification, suggest metabolic targets, and clarify mechanisms behind a debated adjunctive therapy.
Research Themes
- Dynamic coagulation phenotyping and prognosis in sepsis
- Metabolic-immune reprogramming and biomarker discovery
- Mechanistic evaluation of adjunctive therapies (vitamin C) via RCT data
Selected Articles
1. Clinical Characteristics and Prognosis of Sepsis Subphenotypes Identified by Coagulation Indicator Trajectories: A Single-Center Retrospective Study.
In a single-center cohort of 3,990 ICU patients with sepsis, group-based trajectory modeling of daily coagulation indices over the first 7 days identified four distinct subphenotypes. These trajectory-defined groups differed in clinical characteristics and prognosis, highlighting the value of dynamic hemostasis profiling for risk stratification.
Impact: This large-scale trajectory-based phenotyping approach operationalizes dynamic coagulation data to uncover clinically meaningful sepsis subtypes, a step toward precision critical care.
Clinical Implications: Dynamic coagulation trajectories could inform early risk stratification, guide anticoagulation or transfusion strategies, and define enrichment criteria for trials targeting sepsis-associated coagulopathy.
Key Findings
- Four coagulation trajectory-based sepsis subphenotypes were identified within the first 7 days after diagnosis.
- Trajectory-defined groups differed in clinical characteristics and prognosis.
- Group-based trajectory modeling applied to daily coagulation parameters enabled data-driven subphenotyping at scale (n=3,990).
Methodological Strengths
- Large cohort size (n=3,990) with standardized ICU data capture
- Use of group-based trajectory models to leverage longitudinal biomarkers
Limitations
- Single-center retrospective design may limit generalizability
- Abstract does not report detailed outcome metrics for each trajectory group
Future Directions: Prospective validation across centers, incorporation of thromboinflammation biomarkers, and testing whether trajectory-informed interventions improve outcomes.
This study aimed to identify new sepsis subphenotypes on the basis of coagulation indicator trajectories and comprise clinical characteristics and prognosis.This retrospective study included patients diagnosed with sepsis admitted to the intensive care unit of Peking Union Medical College Hospital from May 2016 to March 2023. Using group-based trajectory models, we classified patients into different subphenotypes on the basis of the dynamic daily changes in coagulation parameters within the first 7 days after sepsis diagnosis. Clinical characteristics and outcomes of patients were compared between subphenotypes.A total of 3,990 patients diagnosed with sepsis were included in this research. Patients were divided into four trajectory groups on the basis of indicator trajectory: Group 1 (
2. Multi-Omics and Clinical Validation Identify Key Glycolysis- and Immune-Related Genes in Sepsis.
An integrative multi-omics pipeline identified five glycolysis-related hub genes (DDX18, EIF3L, MAK16, THUMPD1, ZNF260) whose reduced expression tracks with neutrophil predominance and lymphopenia in sepsis. Findings were validated by RT-qPCR in a clinical cohort and contextualized by scRNA-seq, highlighting a metabolic–immune axis in sepsis pathogenesis.
Impact: The study unifies metabolic and immune dysregulation through validated gene signatures, offering concrete, testable targets for therapeutic modulation and patient stratification.
Clinical Implications: Peripheral blood assays targeting the identified gene set could aid early risk stratification and track immune-metabolic status; the genes may inform development of metabolism-directed therapies.
Key Findings
- Glycolysis ssGSEA scores were markedly reduced in sepsis.
- Five hub genes (DDX18, EIF3L, MAK16, THUMPD1, ZNF260) were identified via integrated WGCNA, MR, and machine learning.
- Decreased expression of these genes correlated with increased neutrophils and decreased lymphocytes.
- RT-qPCR in a clinical sepsis cohort validated expression patterns; scRNA-seq mapped cell type–specific alterations.
Methodological Strengths
- Integrative multi-omics pipeline combining ssGSEA, WGCNA, MR, and machine learning
- Independent dataset and clinical RT-qPCR validation with immune cell deconvolution and scRNA-seq context
Limitations
- Exact clinical sample size and cohort characteristics are not specified in the abstract
- Observational and bioinformatic design precludes causal inference
Future Directions: Prospective studies to validate the gene panel as a diagnostic/prognostic tool and interventional trials targeting the glycolysis–immune axis.
BACKGROUND: Sepsis is characterized by profound immune and metabolic perturbations, with glycolysis serving as a pivotal modulator of immune responses. However, the molecular mechanisms linking glycolytic reprogramming to immune dysfunction remain poorly defined. METHODS: Transcriptomic profiles of sepsis were obtained from the Gene Expression Omnibus. Differentially expressed genes (DEGs) related to glycolysis were identified through a combination of ssGSEA, WGCNA and differential expression analysis. Hub genes were prioritized using Mendelian randomization and machine learning algorithms (LASSO, SVM-RFE, and Boruta), and validated in an independent dataset and by RT-qPCR in a clinical sepsis cohort. Immune cell infiltration was assessed using CIBERSORT to profile the immune landscape, and single-cell RNA sequencing (scRNA-seq) was employed to delineate the cell type-specific transcriptional profiles. RESULTS: The ssGSEA scores derived from the glycolysis signature indicated a marked reduction in glycolytic activity associated with sepsis. By employing an integrative framework that includes WGCNA, differential expression analysis, Mendelian randomization, and machine learning algorithms, this study successfully identified five pivotal genes associated with glycolysis: DDX18, EIF3L, MAK16, THUMPD1, and ZNF260. The diminished expression of these genes was significantly correlated with immune remodeling, characterized by an increase in neutrophils and a decrease in lymphocytes. In a clinical sepsis cohort, RT-qPCR of peripheral blood, in conjunction with routine hematological profiling, validated their expression pattern and immune associations. Moreover, scRNA-seq facilitated a comprehensive characterization of these transcriptional alterations within distinct subsets of immune cells. CONCLUSION: This study identifies five glycolysis-related genes linked to immune remodeling in sepsis, revealing a metabolic-immune axis that may drives disease pathogenesis and offers promising targets for therapeutic intervention.
3. Vitamin C Does Not Affect Platelet Counts in Patients With Sepsis: A Post hoc Analysis of the Lessening Organ Dysfunction With Vitamin C Randomized Trial.
In a post hoc analysis of the LOVIT RCT (859 patients with platelet data), longitudinal platelet trajectories were associated with 28-day mortality, but vitamin C administration did not alter platelet counts. This argues against platelet-mediated mechanisms for the adverse outcomes observed with vitamin C in septic shock.
Impact: By leveraging randomized trial data, this study delivers a clear negative mechanistic finding, narrowing plausible pathways for vitamin C–associated harm in septic shock.
Clinical Implications: Clinicians should not expect vitamin C to modulate platelet counts in septic shock; any risk–benefit assessment should consider mechanisms other than thrombocytopathy.
Key Findings
- Among 863 LOVIT participants, 859 had available platelet data.
- Longitudinal platelet count trajectory correlated with 28-day mortality.
- Vitamin C (50 mg/kg q6h for 4 days) did not affect platelet counts compared with placebo.
- Findings do not support platelet-mediated mechanisms for vitamin C–associated adverse outcomes.
Methodological Strengths
- Analysis anchored in a multicenter randomized controlled trial dataset
- Longitudinal modeling of platelet trajectories linked to mortality
Limitations
- Post hoc design limits causal inference for platelet-mediated pathways
- Abstract truncation precludes exact reporting of effect sizes (e.g., HR units)
Future Directions: Investigate alternative mechanisms (e.g., pro-oxidant effects, microcirculatory dysfunction) underlying vitamin C–associated harm and explore patient stratification by redox phenotypes.
OBJECTIVE: Vitamin C has been linked to alterations in platelet count and aggregation behavior. Given recent findings suggesting an association between vitamin C and adverse outcomes in patients with septic shock, we aimed to investigate whether vitamin C influences mortality in septic patients through its impact on platelets. DESIGN: Post hoc analysis of the Lessening Organ Dysfunction With Vitamin C (LOVIT) randomized trial (clinicaltrials.gov NCT03680274). SETTING: Multicenter international study. PATIENTS: Patients were included with an ICU stay of more than 24 hours, confirmed or suspected infection, vasopressor requirement, and availability of platelet count data. INTERVENTION: Vitamin C (50 mg/kg body weight) every 6 hours for 4 days, or placebo. MEASUREMENTS AND MAIN RESULTS: Of the 863 patients enrolled in the LOVIT trial, 859 had available platelet count data at any time. Although the longitudinal trajectory of platelet count was significantly associated with 28-day mortality (hazard ratio 0.97 per 10 × 10 CONCLUSIONS: These results do not support the hypothesis that vitamin C administration increases mortality risk by affecting platelet count.