Daily Sepsis Research Analysis
Analyzed 59 papers and selected 3 impactful papers.
Summary
Analyzed 59 papers and selected 3 impactful articles.
Selected Articles
1. Brain and Muscle ARNT-Like 1 Ameliorates Sepsis-Induced Acute Lung Injury by Orchestrating Endoplasmic Reticulum-Phagy and Mitochondrial Metabolism.
BMAL1 levels were reduced in SI-ALI, independently predicted 28-day mortality (AUC 0.8177), and inversely correlated with SOFA scores. Pharmacologic activation of BMAL1 with nobiletin improved survival and lung pathology in preclinical models by restoring FAM134B-mediated ER-phagy and mitochondrial respiration.
Impact: This study links circadian control to organelle quality control in sepsis-induced lung injury, providing both a prognostic biomarker and a plausible therapeutic axis. The dual clinical–mechanistic design strengthens translational impact.
Clinical Implications: BMAL1 may aid risk stratification in SI-ALI and motivates trials of circadian/ER-phagy-targeting strategies (e.g., nobiletin) while emphasizing timing and organellar homeostasis in critical care interventions.
Key Findings
- BMAL1 and CLOCK expression were significantly reduced in SI-ALI; BMAL1 predicted 28-day mortality (AUC 0.8177) and inversely correlated with SOFA.
- Nobiletin activation of BMAL1 improved 5-day survival, mitigated lung histopathology, and enhanced macrophage phagocytic/bactericidal function in preclinical models.
- BMAL1 deficiency impaired FAM134B-mediated ER-phagy, increased IRE1 signaling, and reduced mitochondrial OXPHOS proteins (NDUFV1, ATP5A) and ATP production; defects were rescued by nobiletin in a BMAL1-dependent manner.
Methodological Strengths
- Prospective clinical cohort combined with randomized, blinded preclinical validation.
- Multimodal mechanistic interrogation (ER-phagy modulation, electron microscopy, Seahorse respiration) linking phenotype to pathway.
Limitations
- Small single-center clinical cohort limits generalizability and external validation.
- Therapeutic findings are preclinical; nobiletin has not been tested in human SI-ALI.
Future Directions: External validation of BMAL1 as a prognostic marker, temporal sampling to capture circadian dynamics, and early-phase trials of ER-phagy/circadian modulators in sepsis-related lung injury.
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. MEASUREMENTS AND MAIN RESULTS: Patients with SI-ALI exhibited profound circadian arrhythmia with significantly reduced expression of BMAL1 and CLOCK. BMAL1 levels were significantly lower in nonsurvivors and served as a robust predictor of 28-day mortality (area under the curve = 0.8177), showing a significant negative correlation with Sequential Organ Failure Assessment scores. In preclinical models, pharmacological activation of BMAL1 via nobiletin significantly mitigated lung histopathological damage, improved 5-day survival, and enhanced macrophage phagocytic and bactericidal activity. Mechanistically, BMAL1 deficiency impaired family with sequence similarity 134, member B-mediated ER-phagy, leading to inositol-requiring enzyme 1 increased and NADH:ubiquinone oxidoreductase core subunit V1, ATP synthase F1 subunit alpha, and seahorse-derived respiration/adenosine triphosphate production decreased. Nobiletin rescued these organellar defects in a BMAL1-dependent manner. CONCLUSIONS: BMAL1 is a master regulator of cellular homeostasis in SI-ALI. It protects against lung injury by orchestrating a coordinated response between ER-phagy and mitochondrial metabolism. BMAL1 represents a clinically valuable prognostic biomarker and a potential therapeutic target for SI-ALI.
2. Plant-Based Diet and Risk of Sepsis: A 16-Year Follow-Up Study.
In 180,442 UK Biobank participants followed a median 13 years, higher adherence to overall and healthy plant-based diet indices was associated with lower incident sepsis (HR 0.87 and 0.84), while unhealthy plant-based diets increased risk (HR 1.15). Mediation analyses implicated metabolic and inflammatory pathways (BMI, CRP).
Impact: This large, well-controlled prospective cohort moves sepsis prevention upstream by identifying diet quality as a modifiable risk factor with biologically plausible mediators.
Clinical Implications: Clinicians can incorporate diet quality counseling—favoring healthy plant-based patterns—into sepsis risk reduction strategies, especially for high-risk individuals, while awaiting interventional evidence.
Key Findings
- Higher overall PDI was associated with lower sepsis risk (HR 0.87; 95% CI 0.79–0.96; p-trend = 0.003).
- Higher hPDI was associated with lower sepsis risk (HR 0.84; 95% CI 0.77–0.93; p-trend < 0.001).
- Higher uPDI was associated with increased sepsis risk (HR 1.15; 95% CI 1.05–1.27; p-trend < 0.001).
- Mediation analysis showed 2.5–31.6% (hPDI) and 1.7–13.7% (uPDI) of associations mediated by metabolic/inflammatory biomarkers including BMI and CRP.
Methodological Strengths
- Very large prospective cohort with long follow-up and robust multivariable adjustment.
- Use of predefined diet indices with trend tests, stratified/sensitivity analyses, and mediation analysis.
Limitations
- Dietary intake was self-reported, introducing measurement error and residual confounding.
- Observational design limits causal inference; UK Biobank selection may reduce generalizability.
Future Directions: Interventional trials testing diet-quality improvement on sepsis incidence and mechanistic studies dissecting immune-metabolic pathways linking diet to infection susceptibility.
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 48,246 ED sepsis encounters at three European centers, EHR-derived SENECA phenotypes showed poor cross-site concordance (Cohen κ 0.32–0.40; Adjusted Rand Index 0.21–0.27). Findings argue against simple transportability of these unsupervised phenotypes and call for alternative subgrouping strategies.
Impact: By demonstrating non-generalizability of widely cited EHR phenotypes, this study reorients precision sepsis efforts toward more stable, transportable stratification methods.
Clinical Implications: Trial enrichment and bedside stratification should not rely on SENECA-style unsupervised clusters without site-specific validation; robust, mechanistically anchored or supervised approaches may be preferable.
Key Findings
- Large multisite cohort (n=48,246) with standardized inclusion criteria replicated k-means clustering into four phenotypes at each site.
- Concordance with SENECA phenotypes was low (Cohen κ: Stockholm 0.32, Oslo 0.37, Oxford 0.40; Adjusted Rand Index 0.21–0.27).
- Phenotypes differed across sites as well, indicating instability and poor transportability of unsupervised EHR-based sepsis phenotypes.
Methodological Strengths
- Large, multicenter replication using identical inclusion criteria and analytic methods.
- Formal concordance metrics (Cohen κ, Adjusted Rand Index) to quantify generalizability.
Limitations
- Retrospective EHR data subject to documentation biases; clustering choices may influence stability.
- Findings limited to three European academic centers; pathogen and practice variation may differ elsewhere.
Future Directions: Develop transportable, mechanistically anchored phenotypes; evaluate supervised/semi-supervised models with external validation and outcome prediction utility.
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. RESULTS: There were 30 865 patient encounters in Stockholm (mean [SD] age, 68 [16] years; 18 165 men [59%]), 15 575 in Oxford (mean [SD] age, 71 [18] years; 9067 men [58%]), and 1806 in Oslo (mean [SD] age, 71 [17] years; 1068 men [59%]). There was little consistency between the SENECA clinical phenotypes and each site's own phenotypes, with a Cohen κ of 0.32 for Stockholm, 0.37 for Oslo, and 0.40 for Oxford; the Adjusted Rand Indices were 0.21 for Stockholm, 0.27 for Oslo, and 0.26 for Oxford. There was also little consistency between the phenotypes derived in Stockholm, Oxford, and Oslo. CONCLUSIONS AND RELEVANCE: This study suggests that the 4 clinical phenotypes of the SENECA data are not generalizable across 3 independent cohorts. This calls for further exploration of possible underlying sepsis subgroups using alternative approaches that mitigate the inherent stochasticity in many unsupervised and semisupervised clustering methods.