Daily Sepsis Research Analysis
Analyzed 59 papers and selected 3 impactful papers.
Summary
Three impactful sepsis studies stood out today: a large multicenter validation shows widely used EHR-derived clinical sepsis phenotypes are not generalizable; a UK Biobank cohort links healthy plant-based diets with reduced future sepsis risk; and a mechanistic-clinical study identifies BMAL1 as a prognostic biomarker and therapeutic node in sepsis-induced acute lung injury via ER-phagy and mitochondrial metabolism.
Research Themes
- Precision sepsis phenotyping and external validation
- Dietary prevention and population risk reduction for sepsis
- Circadian-immunometabolic mechanisms driving organ injury in sepsis
Selected Articles
1. Brain and Muscle ARNT-Like 1 Ameliorates Sepsis-Induced Acute Lung Injury by Orchestrating Endoplasmic Reticulum-Phagy and Mitochondrial Metabolism.
BMAL1 expression was markedly reduced in SI-ALI patients, predicted 28-day mortality (AUC 0.818), and inversely correlated with SOFA. Pharmacologic BMAL1 activation by nobiletin improved lung pathology, survival, and macrophage function in sepsis models. Mechanistically, BMAL1 preserved FAM134B-mediated ER-phagy and mitochondrial respiration; its loss drove ER stress and bioenergetic failure.
Impact: This study links circadian control to sepsis lung injury with dual clinical and mechanistic evidence, nominating BMAL1 as both a prognostic biomarker and a druggable target.
Clinical Implications: BMAL1 measurements could support risk stratification in SI-ALI, and circadian/clock-targeted therapies (e.g., nobiletin) merit translational evaluation for organ protection in sepsis.
Key Findings
- BMAL1 and CLOCK were significantly reduced in SI-ALI and BMAL1 predicted 28-day mortality (AUC 0.8177), inversely correlating with SOFA.
- Nobiletin-mediated BMAL1 activation reduced lung injury, improved 5-day survival, and enhanced macrophage phagocytic and bactericidal activity.
- BMAL1 deficiency impaired FAM134B-mediated ER-phagy, increased IRE1 signaling, and decreased mitochondrial respiratory capacity; nobiletin rescued defects in a BMAL1-dependent manner.
Methodological Strengths
- Combined prospective clinical cohort with randomized, blinded preclinical experiments
- Mechanistic depth spanning ER-phagy, mitochondrial assays, and functional macrophage readouts
Limitations
- Small, single-center clinical cohort limits generalizability
- Translational gap: BMAL1-targeted therapy not yet tested in humans
Future Directions: Validate BMAL1 as a prognostic biomarker in multicenter cohorts and test clock-targeted agents (e.g., nobiletin) in early-phase SI-ALI/sepsis trials with ER-phagy/mitochondrial endpoints.
OBJECTIVES: To evaluate the clinical prognostic value of the core circadian transcription factor brain and muscle ARNT-like 1 (BMAL1) in sepsis-induced acute lung injury (SI-ALI) and explore its mechanistic role in orchestrating organellar homeostasis and macrophage resilience. DESIGN: Prospective clinical cohort study and randomized blinded preclinical laboratory investigation. SETTING: ICU and research laboratory of Renmin Hospital of Wuhan University. SUBJECTS: Thirty patients with SI-ALI and 12 healthy controls; adult male C57BL/6 mice and mouse alveolar macrophage cell line (MH-S) alveolar macrophages. INTERVENTIONS: Clinical monitoring of BMAL1, clock circadian regulator (CLOCK) genes, and hormones. Murine cecal ligation and puncture models, lipopolysaccharide treated MH-S cell treated with nobiletin, small interfering RNA-mediated knockdown of BMAL1, and pharmacological modulators of endoplasmic reticulum (ER)-phagy.
2. Plant-Based Diet and Risk of Sepsis: A 16-Year Follow-Up Study.
In 180,442 UK Biobank participants followed for a median 13 years, higher adherence to overall and healthy plant-based diets was associated with lower sepsis risk, while unhealthy plant-based patterns were associated with higher risk. Mediation analyses implicated metabolic and inflammatory pathways.
Impact: This large prospective cohort provides robust population-level evidence that dietary quality modulates future sepsis risk, opening a preventive avenue beyond acute care.
Clinical Implications: Clinicians can incorporate high-quality plant-based dietary counseling into risk-reduction strategies for infection-prone adults, alongside trials to test causality and implementation in high-risk groups.
Key Findings
- Higher overall PDI and healthy PDI were associated with lower sepsis risk (HR 0.87 and 0.84, respectively).
- Unhealthy plant-based diet index was associated with higher sepsis risk (HR 1.15).
- Mediation analysis suggested 2.5–31.6% of effects may operate via BMI and C-reactive protein and related biomarkers.
Methodological Strengths
- Very large sample size with long-term prospective follow-up
- Comprehensive multivariable adjustment, stratified and sensitivity analyses, and mediation analysis
Limitations
- Dietary intake was self-reported, risking measurement error
- Residual confounding and limited generalizability beyond UK population are possible
Future Directions: Test dietary interventions in high-risk populations for sepsis, evaluate diet–microbiome–immune axes, and integrate diet quality into sepsis risk prediction tools.
OBJECTIVES: Plant-based diets have been linked to favorable metabolic and immune regulation, suggesting their potential role in sepsis prevention. However, evidence supporting this association remains limited. This study aimed to examine the associations between adherence to plant-based dietary patterns and risk of sepsis. DESIGN: A large-scale cohort study. SETTING: This was a prospective cohort study including participants of the UK Biobank. PATIENTS: A total of 180,442 participants from the UK Biobank. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The overall, healthy, and unhealthy plant-based diet indices (PDI, hPDI, and uPDI, respectively) were constructed leveraging self-reported data on 17 major food groups. Multivariable-adjusted Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) and 95% CIs for the associations between PDIs and risk of sepsis. During a median follow-up of 13 years, 4031 incident cases of sepsis were identified. A greater adherence to PDI or hPDI was associated with a lower risk of sepsis (PDI: HR, 0.87; 95% CI, 0.79-0.96; p-trend = 0.003 and hPDI: HR, 0.84; 95% CI, 0.77-0.93; p-trend < 0.001) after multivariable adjustment. In contrast, a greater adherence to uPDI was associated with an increased sepsis risk (HR, 1.15; 95% CI, 1.05-1.27; p-trend < 0.001). These associations were generally consistent across stratified and sensitivity analyses. Mediation analysis revealed that 2.5-31.6% of the association between hPDI and sepsis and 1.7-13.7% of the association between uPDI and sepsis were mediated via metabolic and inflammatory biomarkers, including body mass index and C-reactive protein. CONCLUSIONS: Adherence to a healthy plant-based diet was associated with a lower risk of sepsis, whereas adherence to an unhealthy plant-based diet was associated with an increased risk, independently of other traditional risk factors. The associations may be partly mediated through metabolic and inflammatory pathways. These findings underscore the role of high-quality plant-based diet in sepsis prevention.
3. Multicenter Validation of Clinical Sepsis Phenotypes.
Across three independent ED cohorts (n≈48,000), there was little concordance between locally derived phenotypes and the original SENECA phenotypes (Cohen κ 0.32–0.40; Adjusted Rand Index 0.21–0.27) and also limited consistency across sites. Findings indicate poor generalizability of the four EHR-derived sepsis phenotypes.
Impact: This negative, multicenter validation challenges the field’s reliance on fixed EHR-derived phenotypes and redirects efforts toward more robust, reproducible subgrouping approaches.
Clinical Implications: Clinicians and trialists should avoid over-reliance on current EHR-derived phenotypes for triage or enrichment; alternative, stable and externally validated stratification methods are needed.
Key Findings
- Poor concordance between site-specific phenotypes and SENECA phenotypes (Cohen κ 0.32–0.40; Adjusted Rand Index 0.21–0.27).
- Limited consistency across Stockholm, Oxford, and Oslo phenotypes, despite identical inclusion criteria and methods.
- Results highlight stochasticity and site effects in unsupervised clustering, calling for alternative subgrouping strategies.
Methodological Strengths
- Large, multisite dataset with standardized inclusion and analytic methods
- Direct external validation of a published phenotyping framework
Limitations
- Retrospective EHR-based design with potential data quality and measurement variability
- Findings confined to European ED populations; therapeutic implications not tested
Future Directions: Develop robust, transportable phenotyping using causal frameworks, temporal modeling, and multi-omic integration; link phenotypes to treatment response in prospective trials.
IMPORTANCE: Four clinical phenotypes of sepsis based on data from electronic health records have been proposed. Although promising, the generalizability of these phenotypes remains uncertain, and multisite validation is needed. OBJECTIVE: To validate, using the same methods and inclusion criteria, the 4 clinical phenotypes derived from Sepsis Endotyping in Emergency Care (SENECA) data. DESIGN, SETTING, AND PARTICIPANTS: This multisite retrospective cohort study uses data on adult patients admitted to the emergency departments in Stockholm, Sweden (January 1, 2011, to September 1, 2023); Oxford, England (February 4, 2014, to June 20, 2021); and Oslo, Norway (January 4, 2019, to October 9, 2023) university hospitals. Included encounters are those with body fluid cultures taken, documented antibiotic administration, and Sequential Organ Failure Assessment scores of 2 or more, all within 6 hours of admission. Data analysis was conducted from November 2, 2023, to September 1, 2025. MAIN OUTCOMES AND MEASURES: Consensus clustering with k-means was used to derive 4 clinical phenotypes at each site, comparing them with the SENECA-derived phenotypes, as well as with one another.