โก Quick Summary
This study utilized plasma proteomics to identify four distinct molecular subtypes in sepsis, revealing significant differences in immune response and disease severity. The findings pave the way for personalized therapies and improved clinical outcomes in sepsis management.
๐ Key Details
- ๐ Dataset: Plasma samples from 333 patients collected on days 1 and 4 of sepsis.
- โ๏ธ Technology: Liquid chromatography coupled to tandem mass spectrometry.
- ๐ Analysis Method: K-means clustering and random forest machine learning classifier.
- ๐ Key Findings: Identification of four sepsis subtypes with varying severity and immune responses.
๐ Key Takeaways
- ๐ Four sepsis subtypes were identified, each with distinct clinical and molecular characteristics.
- ๐ก Cluster 0 had a 100% mortality rate, indicating the most severe form of sepsis.
- ๐งฌ Cluster 1 showed significant activation of the adaptive immune system, with elevated immunoglobulin levels.
- ๐ฅ Cluster 2 was marked by acute inflammation and the lowest immunoglobulin levels.
- ๐ Cluster 3 represented the baseline proteome of the cohort.
- ๐ค A machine learning classifier was developed using just 10 proteins for effective patient assignment to subtypes.
- ๐ Findings enhance understanding of immune responses and disease mechanisms in sepsis.
- ๐ฎ Potential for targeted therapies and predictive enrichment in clinical trials.
๐ Background
Sepsis is a complex and heterogeneous condition that poses a significant challenge in clinical settings. The variability in patient responses complicates the development of effective, personalized therapies. Recent advancements in proteomics offer a promising avenue for identifying molecular subtypes of sepsis, which could lead to more tailored treatment approaches and improved patient outcomes.
๐๏ธ Study
This study was conducted using a prospective multi-center cohort of sepsis patients. Plasma samples were collected from 333 individuals on the first and fourth days of their sepsis diagnosis. The researchers employed advanced liquid chromatography coupled to tandem mass spectrometry to analyze the plasma proteome and identify distinct subtypes through K-means clustering.
๐ Results
The analysis revealed four distinct subtypes of sepsis, each associated with varying degrees of severity and immune response. Notably, Cluster 0 was identified as the most severe, with a concerning 100% mortality rate. In contrast, Cluster 1 exhibited a robust adaptive immune response, while Cluster 2 was characterized by acute inflammation. The machine learning classifier developed in this study demonstrated high confidence in assigning patients to their respective clusters based on just 10 proteins and immunoglobulin levels.
๐ Impact and Implications
The identification of these plasma proteome subtypes has profound implications for the future of sepsis treatment. By providing insights into the underlying immune responses and disease mechanisms, this research supports the development of targeted therapies that can be tailored to individual patient profiles. Furthermore, the ability to predict patient outcomes based on proteomic data could enhance clinical trial designs and improve therapeutic efficacy.
๐ฎ Conclusion
This study marks a significant advancement in our understanding of sepsis through the lens of plasma proteomics. The identification of molecular subtypes not only sheds light on the complex nature of sepsis but also opens the door to personalized medicine approaches that could transform patient care. Continued research in this area is essential for developing effective interventions and improving survival rates in sepsis patients.
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Plasma proteomics identifies molecular subtypes in sepsis.
Abstract
BACKGROUND: The heterogeneity of sepsis represents a significant challenge to the development of personalized sepsis therapies. Sepsis subtyping has therefore emerged as an important approach to this problem, but its impact on clinical practice was limited due to insufficient molecular insights. Modern proteomics techniques allow the identification of subtypes and provide molecular and mechanistical insights. In this study, we analyzed a prospective multi-center sepsis cohort using plasma proteomics to describe and characterize sepsis plasma proteome subtypes.
METHODS: Plasma samples were collected from 333 patients at days 1 and 4 of sepsis and analyzed using liquid chromatography coupled to tandem mass spectrometry. Plasma proteome subtypes were identified using K-means clustering and characterized based on clinical routine data, cytokine measurements, and proteomics data. A random forest machine learning classifier was generated to showcase future assignment of patients to subtypes.
RESULTS: Four subtypes with different sepsis severity were identified. Cluster 0 represented the most severe form of sepsis, with 100% mortality. Cluster 1, 2 and 3 showed a gradual decrease of the median SOFA score, as reflected by clinical data and cytokine measurements. At the proteome level, the subtypes were characterized by distinct molecular features. We observed an alternating immune response, with cluster 1 showing prominent activation of the adaptive immune system, as indicated by elevated levels immunoglobulin (Ig) levels, which were verified using orthogonal measurements. Cluster 2 was characterized by acute inflammation and the lowest Ig levels. Cluster 3 represented the sepsis proteome baseline of the investigated cohort. We generated an ML classifier and optimized it for the minimum number of proteins that could realistically be implemented into routine diagnostics. The model, which was based on 10 proteins and Ig quantities, allowed the assignment of patients to clusters 1, 2 and 3 with high confidence.
CONCLUSION: The identified plasma proteome subtypes provide insights into the immune response and disease mechanisms and allow conclusions on appropriate therapeutic measures, enabling predictive enrichment in clinical trials. Thus, they represent a step forward in the development of targeted therapies and personalized medicine for sepsis.
Author: [‘Bracht T’, ‘Kappler K’, ‘Bayer M’, ‘Grell F’, ‘Schork K’, ‘Palmowski L’, ‘Koos B’, ‘Rahmel T’, ‘Ziehe D’, ‘Unterberg M’, ‘Bergmann L’, ‘Rump K’, ‘Broecker-Preuss M’, ‘Limper U’, ‘Henzler D’, ‘Ehrentraut SF’, ‘von Groote T’, ‘Zarbock A’, ‘Pfaender S’, ‘Babel N’, ‘Marcus-Alic K’, ‘Eisenacher M’, ‘Adamzik M’, ‘Sitek B’, ‘Nowak H’]
Journal: Crit Care
Citation: Bracht T, et al. Plasma proteomics identifies molecular subtypes in sepsis. Plasma proteomics identifies molecular subtypes in sepsis. 2025; 29:392. doi: 10.1186/s13054-025-05639-6