Towards precision medicine in antiphospholipid syndrome

Area of Science:

• Immunology
• Genetics
• Precision Medicine

Background:

• Antiphospholipid syndrome (APS) is a rare autoimmune disorder with diverse clinical presentations and treatment outcomes.
• Patient heterogeneity suggests underlying differences in pathophysiology and genetics.
• Precision medicine aims to address these variations for tailored therapies.

Purpose of the Study:

• To review current evidence on stratifying APS patients using clinical, genetic, and biomolecular data.
• To explore the role of artificial intelligence in guiding precision medicine for APS.

Main Methods:

• Review of existing literature on APS pathophysiology, subphenotyping, and omic technologies.
• Analysis of clinical, genetic, epigenetic, and molecular profiling data.
• Assessment of artificial intelligence applications in personalized APS treatment.

Main Results:

• Advances in omic technologies and microarray analysis have identified key genes, epigenetic variations, and biomarkers.
• Patient stratification based on phenotypes and molecular profiles enables more effective treatment tailoring.
• Artificial intelligence algorithms show promise in integrating complex data for personalized therapeutic strategies.

Conclusions:

• Stratification of antiphospholipid syndrome patients is crucial for advancing precision medicine.
• Integrating clinical, genetic, and molecular data with AI can optimize treatment efficacy and minimize toxicity.
• Further research into patient subphenotypes and AI integration is essential for personalized APS management.