Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in...

Area of Science:

• Computational Biology
• Clinical Medicine
• Metabolomics

Background:

• Sepsis is a life-threatening condition with high mortality, often caused by bacterial infections.
• Accurate and rapid pathogen identification in sepsis is challenging, hindering timely treatment.
• Microbial activity in sepsis induces detectable metabolomic changes.

Purpose of the Study:

• To develop and validate machine learning models for identifying sepsis pathogens.
• To utilize clinical and metabolomic data for accurate sepsis diagnosis and pathogen discrimination.
• To identify key biosignatures differentiating bacterial types in septic patients.

Main Methods:

• Machine learning classifiers were trained using a clinical-metabolomic database from sepsis patients.
• Data included clinical indicators and metabolite concentrations from 100 infected patients and 29 controls.
• Biosignatures were selected to discriminate microorganisms, and diagnostic performance was evaluated using sensitivity, specificity, and AUC.

Main Results:

• Machine learning-selected biosignatures demonstrated diagnostic value in identifying infected patients (AUC = 0.94 ± 0.054).
• The models successfully differentiated Gram-positive from Gram-negative infections (AUC = 0.80 ± 0.085).
• Enrichment analyses revealed sepsis is associated with abnormal nitrogen metabolism, respiratory disorders, and organ failure.

Conclusions:

• Clinical and metabolomic characteristics identified by machine learning serve as powerful biomarkers for sepsis diagnosis.
• This approach enhances the ability to discriminate between different types of bacterial pathogens in sepsis.
• Findings suggest potential for improved sepsis management through early and accurate pathogen identification.