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Author Spotlight: Self-Assessment Protocol for Predicting Psoriatic Arthritis in Psoriasis Patients
Published on: March 1, 2024
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Molecular Biomarkers for Predicting Treatment Response in Psoriatic Arthritis
Andre Lucas Ribeiro1, Anne Barton2, Jochen M Schwenk3
1Hospital de Clínicas de Porto Alegre, 50 Prof. Duplan Street, Unit 609, Porto Alegre 90420-030, Brazil.
Rheumatic Diseases Clinics of North America
|April 17, 2026
Summary
Predicting treatment response in psoriatic disease requires personalized medicine. This review explores molecular biomarkers for treatment success, highlighting current limitations and future research directions.
Area of Science:
- Rheumatology
- Dermatology
- Immunology
Background:
- Predicting treatment response in psoriatic disease remains a significant clinical challenge.
- Personalized medicine approaches are crucial for optimizing patient outcomes.
- Molecular biomarkers offer potential for tailoring therapies.
Purpose of the Study:
- To review the current literature on biomarkers for predicting treatment response in psoriatic disease.
- To discuss the role of personalized medicine in managing psoriatic conditions.
- To highlight emerging biomarkers and future research challenges.
Main Methods:
- Comprehensive literature search of studies on biomarkers for psoriatic disease treatment response.
- Analysis of existing candidate biomarkers and their limitations.
- Identification of novel and emerging biomarker research avenues.
Main Results:
- The review details various molecular biomarkers investigated for predicting response to therapies in psoriatic disease.
- Current biomarkers show promise but have limitations in specificity and sensitivity.
- Emerging biomarkers and advanced research methodologies are discussed.
Conclusions:
- Biomarker research is essential for advancing personalized medicine in psoriatic disease.
- Overcoming limitations of current biomarkers is key to improving treatment prediction.
- Future research should focus on validating novel biomarkers and integrating multi-omics data.
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