Diversity and molecular network patterns of symptom phenotypes

Area of Science:

• Symptom science
• Network medicine
• Precision health

Background:

• Symptom phenotypes are crucial for diagnosis and management.
• Non-specificity of symptoms presents a challenge for diagnosis and advancing symptom science.
• Network medicine offers a framework for understanding complex disease mechanisms.

Purpose of the Study:

• To construct a phenotype network of symptoms using clinical co-occurrence data.
• To explore molecular network patterns of symptom phenotypes by integrating gene and protein interaction data.
• To quantify phenotypic and molecular diversity of symptoms using network diversity measures.

Main Methods:

• Extracted symptom co-occurrences from clinical textbooks.
• Constructed a phenotype network of symptoms.
• Incorporated symptom-gene associations and protein-protein interactions.
• Applied network diversity measures to quantify phenotypic and molecular diversity.

Main Results:

• Clinical diversity of symptom phenotypes is partially explained by molecular network diversity (PCC = 0.49, P-value = 2.14E-08).
• Non-specific symptoms like chill, vomiting, and amnesia exhibit high phenotypic and molecular network diversity.
• Symptom clustering was validated as a method to reduce symptom non-specificity.

Conclusions:

• Network diversity is a valuable approach for evaluating symptom non-specificity.
• Understanding molecular network mechanisms of symptoms can advance symptom science.
• This approach supports the development of precision health strategies.